As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
CYBER SECURITY TRANDS FOR FUTURE SMART GRID SYSTEMSGeorge Wainblat
SUMMARY - Current power grids increasingly emerging into smart networked grids and are more accessible from the public internet which poses new cyber threats in the grid. More computer based systems are introduced into power networks in order to monitor and control the network. Future model smart grid and micro grid systems will be based on data flows for communication of system status, usage and control throughout the network infrastructure in addition to the power flow. This creates new security threats on the power grid. Instead of relying mainly on power plants for power generation, there will be a combination of multiple generation sources and at the same time wider use of electrical computer based equipment by consumers. Both increase the amount of data flows in the network as well as introduce additional vulnerable spots. Vulnerability of the power grid to cyber-attacks increases even more because of the wide use of SCADA networks. SCADA networks are more accessible to the internet and lack authentication and authorization mechanisms therefore expose the grid to threats such as DDOS, Data interception, Data alteration and additional hacking threats.
The transition from present to future model has already begun and rapidly growing while it already poses new security challenges which must be attended immediately. It is essential to introduce immediately a single comprehensive security solution which will provide fast detection and prevention tools to cope with a variety of threats with different nature and from multiple sources. The solution should not be tightly coupled with each device in the network so it won’t require upgrade of the devices inside the grid.
The Cyber defense solution should be versatile using variety of cyber technologies such as Firewalls, anomaly detection, Big Data analytics, machine learning and more in a network wise combination.
Supervisory control and data acquisition (SCADA) are applications that collect data from a system in order to automate the monitoring and controlling of its activities. Several industrial fields such as, electric utilities, water supplies and buildings' facilities have already adopted SCADA systems to increase the efficiency and reduce cost. However, the IT community is concerned about the level of security that any applied SCADA system provides. This paper concentrates on the major security threats encountered in SCADA systems. In addition, it discusses a new proposed methodology in order to increase the system security with minimal impact on efficiency. The proposed scheme provides several security services which are mutual authentication, confidentiality, data integrity and accountability.
Supervisory control and data acquisition (SCADA) are applications that collect data from a system in order to automate the monitoring and controlling of its activities. Several industrial fields such as, electric utilities, water supplies and buildings’ facilities have already adopted SCADA systems to increase the efficiency and reduce cost. However, the IT community is concerned about the level of security that any applied SCADA system provides. This paper concentrates on the major security threats encountered in SCADA systems. In addition, it discusses a new proposed methodology in order to increase the system security with minimal impact on efficiency. The proposed scheme provides several security services which are mutual authentication, confidentiality, data integrity and accountability.
A data quarantine model to secure data in edge computingIJECEIAES
Edge computing provides an agile data processing platform for latencysensitive and communication-intensive applications through a decentralized cloud and geographically distributed edge nodes. Gaining centralized control over the edge nodes can be challenging due to security issues and threats. Among several security issues, data integrity attacks can lead to inconsistent data and intrude edge data analytics. Further intensification of the attack makes it challenging to mitigate and identify the root cause. Therefore, this paper proposes a new concept of data quarantine model to mitigate data integrity attacks by quarantining intruders. The efficient security solutions in cloud, ad-hoc networks, and computer systems using quarantine have motivated adopting it in edge computing. The data acquisition edge nodes identify the intruders and quarantine all the suspected devices through dimensionality reduction. During quarantine, the proposed concept builds the reputation scores to determine the falsely identified legitimate devices and sanitize their affected data to regain data integrity. As a preliminary investigation, this work identifies an appropriate machine learning method, linear discriminant analysis (LDA), for dimensionality reduction. The LDA results in 72.83% quarantine accuracy and 0.9 seconds training time, which is efficient than other state-of-the-art methods. In future, this would be implemented and validated with ground truth data.
The document proposes a Portable and Data Security Tolerance-based Energy-Efficient Framework (PDST-EEF) for maintaining data security while lowering energy usage of sensor devices in smart grid environments. PDST is developed to ensure data privacy using an authentication method integrated with cryptographic signatures to detect various attacks like denial of service and replay attacks. EEF then presents an energy-efficient cybersecurity mechanism for sensor networks in smart grids. It identifies malicious nodes, forms groups, and allocates keys with less energy. Experimental results show that PDST-EEF improves authentication time by a second with 5.06% less energy usage. The framework aims to provide high-level security for sensor networks while minimizing their energy consumption.
Data security tolerance and portable based energy-efficient framework in sens...Venu Madhav
Wireless Sensor Networks (WSNs) are effective devices used for remote surveillance, device failure prediction,
and housing energy control in numerous smart grid implementations. Several interaction structures and remedies,
such as broadband networks, cable networks, Wireless Sensor networks, have been suggested to assist
Smart Grid implementations. Owing to their cheap, dynamic nature, robustness, and low energy profile, WSNs
are attractive devices, and preserving a low energy pattern is an essential factor in WSN. Implementing quality
services and safety techniques in sensor networks is challenging in smart grid applications. Thus, in this article,
Portable and Data Security Tolerancebased Energy-Efficient Framework(PDST-EEF) has been proposed for
maintaining a high standard of data security by lowering the sensor device energy usage in smart grid surroundings.
PDST model is developed to ensure data privacy in sensor networks by utilizing an authentication
method integrated with the cryptographic signature model to detect the various attacks. PDST identifies and
separates attacks like denial of service and replay efficiently. EEF presents a low-power cyber safety mechanism
on sensor networks with smart grid tracking applications. EEF is modeled with different stages like identifying
anti-nodes, group development, and allocating keys less energy. EEF can operate with higher power efficiency
techniques while preserving sustained throughput and reliability ideals. The experimental result shows that the
PDST-EEF’s specific request and authentication period is often enhanced by just a second with less energy usage
of 5.06%.
Three level intrusion detection system based on conditional generative advers...IJECEIAES
Security threat protection is important in the internet of things (IoT) applications since both the connected device and the captured data can be hacked or hijacked or both at the same time. To tackle the above-mentioned problem, we proposed three-level intrusion detection system conditional generative adversarial network (3LIDS-CGAN) model which includes four phases such as first-level intrusion detection system (IDS), second-level IDS, third-level IDS, and attack type classification. In first-level IDS, features of the incoming packets are extracted by the firewall. Based on the extracted features the packets are classified into three classes such as normal, malicious, and suspicious using support vector machine and golden eagle optimization. Suspicious packets are forwarded to the second-level IDS which classified the suspicious packets as normal or malicious. Here, signature-based intrusions are detected using attack history information, and anomaly-based intrusions are detected using event-based semantic mapping. In third-level IDS, adversary packets are detected using CGAN which automatically learns the adversarial environment and detects adversary packets accurately. Finally, proximal policy optimization is proposed to detect the attack type. Experiments are conducted using the NS-3.26 network simulator and performance is evaluated by various performance metrics which results that the proposed 3LIDS-CGAN model outperforming other existing works.
Hyperparameters optimization XGBoost for network intrusion detection using CS...IAESIJAI
With the introduction of high-speed internet access, the demand for security and dependable networks has grown. In recent years, network attacks have gotten more complex and intense, making security a vital component of organizational information systems. Network intrusion detection systems (NIDS) have become an essential detection technology to protect data integrity and system availability against such attacks. NIDS is one of the most well-known areas of machine learning software in the security field, with machine learning algorithms constantly being developed to improve performance. This research focuses on detecting abnormalities in societal infiltration using the hyperparameters optimization XGBoost (HO-XGB) algorithm with the Communications Security Establishment-The Canadian Institute for Cybersecurity-Intrusion Detection System2018 (CSE-CICIDS2018) dataset to get the best potential results. When compared to typical machine learning methods published in the literature, HO-XGB outperforms them. The study shows that XGBoost outperforms other detection algorithms. We refined the HO-XGB model's hyperparameters, which included learning_rate, subsample, max_leaves, max_depth, gamma, colsample_bytree, min_child_weight, n_estimators, max_depth, and reg_alpha. The experimental findings reveal that HO-XGB1 outperforms multiple parameter settings for intrusion detection, effectively optimizing XGBoost's hyperparameters.
CYBER SECURITY TRANDS FOR FUTURE SMART GRID SYSTEMSGeorge Wainblat
SUMMARY - Current power grids increasingly emerging into smart networked grids and are more accessible from the public internet which poses new cyber threats in the grid. More computer based systems are introduced into power networks in order to monitor and control the network. Future model smart grid and micro grid systems will be based on data flows for communication of system status, usage and control throughout the network infrastructure in addition to the power flow. This creates new security threats on the power grid. Instead of relying mainly on power plants for power generation, there will be a combination of multiple generation sources and at the same time wider use of electrical computer based equipment by consumers. Both increase the amount of data flows in the network as well as introduce additional vulnerable spots. Vulnerability of the power grid to cyber-attacks increases even more because of the wide use of SCADA networks. SCADA networks are more accessible to the internet and lack authentication and authorization mechanisms therefore expose the grid to threats such as DDOS, Data interception, Data alteration and additional hacking threats.
The transition from present to future model has already begun and rapidly growing while it already poses new security challenges which must be attended immediately. It is essential to introduce immediately a single comprehensive security solution which will provide fast detection and prevention tools to cope with a variety of threats with different nature and from multiple sources. The solution should not be tightly coupled with each device in the network so it won’t require upgrade of the devices inside the grid.
The Cyber defense solution should be versatile using variety of cyber technologies such as Firewalls, anomaly detection, Big Data analytics, machine learning and more in a network wise combination.
Supervisory control and data acquisition (SCADA) are applications that collect data from a system in order to automate the monitoring and controlling of its activities. Several industrial fields such as, electric utilities, water supplies and buildings' facilities have already adopted SCADA systems to increase the efficiency and reduce cost. However, the IT community is concerned about the level of security that any applied SCADA system provides. This paper concentrates on the major security threats encountered in SCADA systems. In addition, it discusses a new proposed methodology in order to increase the system security with minimal impact on efficiency. The proposed scheme provides several security services which are mutual authentication, confidentiality, data integrity and accountability.
Supervisory control and data acquisition (SCADA) are applications that collect data from a system in order to automate the monitoring and controlling of its activities. Several industrial fields such as, electric utilities, water supplies and buildings’ facilities have already adopted SCADA systems to increase the efficiency and reduce cost. However, the IT community is concerned about the level of security that any applied SCADA system provides. This paper concentrates on the major security threats encountered in SCADA systems. In addition, it discusses a new proposed methodology in order to increase the system security with minimal impact on efficiency. The proposed scheme provides several security services which are mutual authentication, confidentiality, data integrity and accountability.
A data quarantine model to secure data in edge computingIJECEIAES
Edge computing provides an agile data processing platform for latencysensitive and communication-intensive applications through a decentralized cloud and geographically distributed edge nodes. Gaining centralized control over the edge nodes can be challenging due to security issues and threats. Among several security issues, data integrity attacks can lead to inconsistent data and intrude edge data analytics. Further intensification of the attack makes it challenging to mitigate and identify the root cause. Therefore, this paper proposes a new concept of data quarantine model to mitigate data integrity attacks by quarantining intruders. The efficient security solutions in cloud, ad-hoc networks, and computer systems using quarantine have motivated adopting it in edge computing. The data acquisition edge nodes identify the intruders and quarantine all the suspected devices through dimensionality reduction. During quarantine, the proposed concept builds the reputation scores to determine the falsely identified legitimate devices and sanitize their affected data to regain data integrity. As a preliminary investigation, this work identifies an appropriate machine learning method, linear discriminant analysis (LDA), for dimensionality reduction. The LDA results in 72.83% quarantine accuracy and 0.9 seconds training time, which is efficient than other state-of-the-art methods. In future, this would be implemented and validated with ground truth data.
The document proposes a Portable and Data Security Tolerance-based Energy-Efficient Framework (PDST-EEF) for maintaining data security while lowering energy usage of sensor devices in smart grid environments. PDST is developed to ensure data privacy using an authentication method integrated with cryptographic signatures to detect various attacks like denial of service and replay attacks. EEF then presents an energy-efficient cybersecurity mechanism for sensor networks in smart grids. It identifies malicious nodes, forms groups, and allocates keys with less energy. Experimental results show that PDST-EEF improves authentication time by a second with 5.06% less energy usage. The framework aims to provide high-level security for sensor networks while minimizing their energy consumption.
Data security tolerance and portable based energy-efficient framework in sens...Venu Madhav
Wireless Sensor Networks (WSNs) are effective devices used for remote surveillance, device failure prediction,
and housing energy control in numerous smart grid implementations. Several interaction structures and remedies,
such as broadband networks, cable networks, Wireless Sensor networks, have been suggested to assist
Smart Grid implementations. Owing to their cheap, dynamic nature, robustness, and low energy profile, WSNs
are attractive devices, and preserving a low energy pattern is an essential factor in WSN. Implementing quality
services and safety techniques in sensor networks is challenging in smart grid applications. Thus, in this article,
Portable and Data Security Tolerancebased Energy-Efficient Framework(PDST-EEF) has been proposed for
maintaining a high standard of data security by lowering the sensor device energy usage in smart grid surroundings.
PDST model is developed to ensure data privacy in sensor networks by utilizing an authentication
method integrated with the cryptographic signature model to detect the various attacks. PDST identifies and
separates attacks like denial of service and replay efficiently. EEF presents a low-power cyber safety mechanism
on sensor networks with smart grid tracking applications. EEF is modeled with different stages like identifying
anti-nodes, group development, and allocating keys less energy. EEF can operate with higher power efficiency
techniques while preserving sustained throughput and reliability ideals. The experimental result shows that the
PDST-EEF’s specific request and authentication period is often enhanced by just a second with less energy usage
of 5.06%.
Three level intrusion detection system based on conditional generative advers...IJECEIAES
Security threat protection is important in the internet of things (IoT) applications since both the connected device and the captured data can be hacked or hijacked or both at the same time. To tackle the above-mentioned problem, we proposed three-level intrusion detection system conditional generative adversarial network (3LIDS-CGAN) model which includes four phases such as first-level intrusion detection system (IDS), second-level IDS, third-level IDS, and attack type classification. In first-level IDS, features of the incoming packets are extracted by the firewall. Based on the extracted features the packets are classified into three classes such as normal, malicious, and suspicious using support vector machine and golden eagle optimization. Suspicious packets are forwarded to the second-level IDS which classified the suspicious packets as normal or malicious. Here, signature-based intrusions are detected using attack history information, and anomaly-based intrusions are detected using event-based semantic mapping. In third-level IDS, adversary packets are detected using CGAN which automatically learns the adversarial environment and detects adversary packets accurately. Finally, proximal policy optimization is proposed to detect the attack type. Experiments are conducted using the NS-3.26 network simulator and performance is evaluated by various performance metrics which results that the proposed 3LIDS-CGAN model outperforming other existing works.
Hyperparameters optimization XGBoost for network intrusion detection using CS...IAESIJAI
With the introduction of high-speed internet access, the demand for security and dependable networks has grown. In recent years, network attacks have gotten more complex and intense, making security a vital component of organizational information systems. Network intrusion detection systems (NIDS) have become an essential detection technology to protect data integrity and system availability against such attacks. NIDS is one of the most well-known areas of machine learning software in the security field, with machine learning algorithms constantly being developed to improve performance. This research focuses on detecting abnormalities in societal infiltration using the hyperparameters optimization XGBoost (HO-XGB) algorithm with the Communications Security Establishment-The Canadian Institute for Cybersecurity-Intrusion Detection System2018 (CSE-CICIDS2018) dataset to get the best potential results. When compared to typical machine learning methods published in the literature, HO-XGB outperforms them. The study shows that XGBoost outperforms other detection algorithms. We refined the HO-XGB model's hyperparameters, which included learning_rate, subsample, max_leaves, max_depth, gamma, colsample_bytree, min_child_weight, n_estimators, max_depth, and reg_alpha. The experimental findings reveal that HO-XGB1 outperforms multiple parameter settings for intrusion detection, effectively optimizing XGBoost's hyperparameters.
As the Supervisory Control and Data Acquisition (SCADA) system are deployed in infrastructures which are critical to the survival of a nation, they have emerged as a potential terrain for cyber-war, thus attracting the considered attention of ‘nation-states’. The analysis of worms like ‘stuxnet’ ‘flame’ and ‘duqu’ reveals the hand of a ‘nation-state’ in their design and deployment. Hence, the necessity to understand various issues in the defence of SCADA systems arises. The forensics of the SCADA system provide deep insight into the design and deployment of the worm (the malware) once the system is attacked. This is precisely the scope of this essay.
Smart Grid Systems Based Survey on Cyber Security IssuesjournalBEEI
The future power system will be an innovative administration of existing power grids, which is called smart grid. Above all, the application of advanced communication and computing tools is going to significantly improve the productivity and consistency of smart grid systems with renewable energy resources. Together with the topographies of the smart grid, cyber security appears as a serious concern since a huge number of automatic devices are linked through communication networks. Cyber attacks on those devices had a direct influence on the reliability of extensive infrastructure of the power system. In this survey, several published works related to smart grid system vulnerabilities, potential intentional attacks, and suggested countermeasures for these threats have been investigated.
AN EFFICIENT SECURE CRYPTOGRAPHY SCHEME FOR NEW ML-BASED RPL ROUTING PROTOCOL...IJNSA Journal
Internet of Things (IoT) offers reliable and seamless communication for the heterogeneous dynamic lowpower and lossy network (LLNs). To perform effective routing in IoT communication, LLN Routing Protocol (RPL) is developed for the tiny nodes to establish connection by using deflaut objective functions: OF0, MRHOF, for which resources are constraints like battery power, computation capacity, memory communication link impacts on varying traffic scenarios in terms of QoS metrics like packet delivery ratio, delay, secure communication channel. At present, conventional Internet of Things (IoT) are having secure communication channels issue for transmission of data between nodes. To withstand those issues, it is necessary to balance resource constraints of nodes in the network. In this paper, we developed a security algorithm for IoT networks with RPL routing. Initially, the constructed network in corporates optimizationbased deep learning (reinforcement learning) for route establishment in IoT. Upon the establishment of the route, the ClonQlearn based security algorithm is implemented for improving security which is based onaECC scheme for encryption and decryption of data. The proposed security technique incorporates reinforcement learning-based ClonQlearnintegrated with ECC (ClonQlearn+ECC) for random key generation. The proposed ClonQlearn+ECCexhibits secure data transmission with improved network performance when compared with the earlier works in simulation. The performance of network expressed that the proposed ClonQlearn+ECC increased the PDR of approximately 8% - 10%, throughput of 7% - 13%, end-to-end delay of 5% - 10% and power consumption variation of 3% - 7%.
This document summarizes a research paper that proposes a design for a secure and sophisticated electricity meter called an Impregnable Device for Secured Metering (IDSM). The IDSM uses a microcontroller integrated with a smart meter to securely transmit power consumption data via a legacy Wi-Fi system. Random number addressing cryptography (RAC) is used for encryption due to its high speed, low power usage, and security. The IDSM system connects individual household meters to a centralized server that calculates billing amounts and sends updates back to the meters for display. The goal is to provide secure metering and billing that reduces human error and electricity theft while lowering costs.
This document summarizes a research paper that proposes a design for a secure, Wi-Fi integrated electricity meter called an Impregnable Device for Secured Metering (IDSM). The IDSM consists of a sophisticated meter with additional security features compared to traditional meters. It uses Wi-Fi communication, a microcontroller, and a centralized monitoring and control unit. Random number addressing cryptography (RAC) is chosen as the most secure encryption technique. The meter in each home connects via a wireless network to a server that calculates billing amounts and sends updates to be displayed on the home meter, reducing labor while increasing transparency. The design aims to provide secure communication at high speeds with an advanced metering system and unique database backend.
Practical analysis of the cybersecurity of European smart gridsSergey Gordeychik
This paper summarizes the experience gained during a series of
practical cybersecurity assessments of various components of Europe’s
smart electrical grids.
SECURITY AND PRIVACY AWARE PROGRAMMING MODEL FOR IOT APPLICATIONS IN CLOUD EN...ijccsa
This document summarizes a research paper on privacy-preserving techniques for IoT data in cloud environments. It introduces two differential privacy algorithms: 1) Generic differential privacy (GenDP) which provides generalized privacy protection for homogeneous and heterogeneous IoT metadata through data portioning. 2) Cluster-based differential privacy which groups similar data into clusters before defining classifiers to validate privacy. The paper evaluates these techniques and finds the cluster-based approach offers better security than customized interactive algorithms while maintaining data utility. Overall, the study presents new differential privacy methods for anonymizing IoT metadata stored in the cloud.
Detecting network attacks model based on a convolutional neural network IJECEIAES
Due to the increasing use of networks at present, Internet systems have raised many security problems, and statistics indicate that the rate of attacks or intrusions has increased excessively annually, and in the event of any malicious attack on network vulnerabilities or information systems, it may lead to serious disasters, violating policies on network security, i.e., “confidentiality, integrity, and availability” (CIA). Therefore, many detection systems, such as the intrusion detection system, appeared. In this paper, we built a system that detects network attacks using the latest machine learning algorithms and a convolutional neural network based on a dataset of the CSE-CIC-IDS2018. It is a recent dataset that contains a set of common and recent attacks. The detection rate is 99.7%, distinguishing between aggressive attacks and natural assertiveness.
Security in Software Defined Networks (SDN): Challenges and Research Opportun...Editor IJCATR
In networks, the rapidly changing traffic patterns of search engines, Internet of Things (IoT) devices, Big Data and data centers has thrown up new challenges for legacy; existing networks; and prompted the need for a more intelligent and innovative way to dynamically manage traffic and allocate limited network resources. Software Defined Network (SDN) which decouples the control plane from the data plane through network vitalizations aims to address these challenges. This paper has explored the SDN architecture and its implementation with the OpenFlow protocol. It has also assessed some of its benefits over traditional network architectures, security concerns and how it can be addressed in future research and related works in emerging economies such as Nigeria.
Intrusion Detection for HealthCare Network using Machine LearningIRJET Journal
1) The document discusses using machine learning techniques for intrusion detection in healthcare networks. It aims to build an effective intrusion detection system that can efficiently detect intrusions and provide safety for sensitive patient health information and medical data.
2) The methodology involves pre-processing the NSL-KDD dataset, training a decision tree classifier model, and using the trained model to predict intrusions. Accuracy of 90.3% was achieved using cross-validation.
3) Future work could include using all dataset features, immediately alerting administrators of attacks, and making the system multi-lingual. The system aims to provide secure access of healthcare data for authorized users and detect unauthorized access attempts.
Automated diagnosis of attacks in internet of things using machine learning a...journalBEEI
The Internet of Things (IoT) is the interconnection of things around us to make our daily process more efficient by providing more comfort and productivity. However, these connections also reveal a lot of sensitive data. Therefore, thinking about the methods of information security and coding are important as the security approaches that rely heavily on coding are not a strong match for these restricted devices. Consequently, this research aims to contribute to filling this gap, which adopts machine learning techniques to enhance network-level security in the low-power devices that use the lightweight MQTT protocol for their work. This study used a set of tools tools and, through various techniques, trained the proposed system ranging from Ensemble methods to deep learning models. The system has come to know what type of attack has occurred, which helps protect IoT devices. The log loss of the Ensemble methods is 0.44, and the accuracy of multi-class classification is 98.72% after converting the table data into an image set. The work also uses a Convolution Neural Network, which has a log loss of 0.019 and an accuracy of 99.3%. It also aims to implement these functions in IDS.
Recent changes to the existing power grid are expected to influence the way energy is provided and consumed by customers. Advanced Metering Infrastructure (AMI) is a tool to incorporate these changes for modernizing the electricity grid. Growing energy needs are forcing government agencies and utility companies to move towards AMI systems as part of larger smart grid initiatives. The smart grid promises to enable a more reliable, sustainable, and efficient power grid by taking advantage of information and communication technologies. However, this information-based power grid can reveal sensitive private information from the user’s perspective due to its ability to gather highly granular power consumption data. This has resulted in limited consumer acceptance and proliferation of the smart grid. Hence, it is crucial to design a mechanism to prevent the leakage of such sensitive consumer usage information in
smart grid. Among different solutions for preserving consumer privacy in Smart Grid Network(SGN), private data aggregation techniques have received a tremendous focus from security researchers. Existing privacy-preserving aggregation mechanisms in SGNs utilize cryptographic techniques, specifically homomorphic properties of public-key cryptosystems. Such homomorphic approaches are bandwidthintensive (due to large output blocks they generate), and in most cases, are computationally complex. In this paper, we present a novel and efficient CDMA-based approach to achieve privacy-preserving aggregation in SGNs by utilizing random perturbation of power consumption data and with limited use of traditional cryptography. We evaluate and validate the efficiency and performance of our proposed privacy preserving data aggregation scheme through extensive statistical analyses and simulations.
Machine learning-based intrusion detection system for detecting web attacksIAESIJAI
The increasing use of smart devices results in a huge amount of data, which raises concerns about personal data, including health data and financial data. This data circulates on the network and can encounter network traffic at any time. This traffic can either be normal traffic or an intrusion created by hackers with the aim of injecting abnormal traffic into the network. Firewalls and traditional intrusion detection systems detect attacks based on signature patterns. However, this is not sufficient to detect advanced or unknown attacks. To detect different types of unknown attacks, the use of intelligent techniques is essential. In this paper, we analyse some machine learning techniques proposed in recent years. In this study, several classifications were made to detect anomalous behaviour in network traffic. The models were built and evaluated based on the Canadian Institute for Cybersecurity-intrusion detection systems dataset released in 2017 (CIC-IDS-2017), which includes both current and historical attacks. The experiments were conducted using decision tree, random forest, logistic regression, gaussian naïve bayes, adaptive boosting, and their ensemble approach. The models were evaluated using various evaluation metrics such as accuracy, precision, recall, F1-score, false positive rate, receiver operating characteristic curve, and calibration curve.
Cyber-Defensive Architecture for Networked Industrial Control SystemsIJEACS
This paper deals with the inevitable consequence of the convenience and efficiency we benefit from the open, networked control system operation of safety-critical applications: vulnerability to such system from cyber-attacks. Even with numerous metrics and methods for intrusion detection and mitigation strategy, a complete detection and deterrence of internal code flaws and outside cyber-attacks has not been found and would not be found anytime soon. Considering the ever incompleteness of detection and prevention and the impact and consequence of mal-functions of the safety-critical operations caused by cyber incidents, this paper proposes a new computer control system architecture which assures resiliency even under compromised situations. The proposed architecture is centered on diversification of hardware systems and unidirectional communication from the proposed system in alerting suspicious activities to upper layers. This paper details the architectural structure of the proposed cyber defensive computer control system architecture for power substation applications and its validation in lab experimentation and on a cybersecurity testbed.
This document proposes using Software Defined Networking (SDN) to improve security in Internet of Things (IoT) networks. It discusses how SDN allows centralized control and programmability that can be used to implement security applications and dynamically enforce security policies. The document presents a framework that uses an SDN controller and edge node running virtual machines. It collects network flow data and uses an anomaly detection algorithm to identify malicious flows based on variance from expected values. When anomalies are detected, security policies are applied through the SDN controller to mitigate the threats, such as rate limiting or blocking malicious traffic flows. Simulation results show the effectiveness of the anomaly detection algorithm improves as the time window size increases.
Security and risk analysis in the cloud with software defined networking arch...IJECEIAES
Cloud computing has emerged as the actual trend in business information technology service models, since it provides processing that is both costeffective and scalable. Enterprise networks are adopting software-defined networking (SDN) for network management flexibility and lower operating costs. Information technology (IT) services for enterprises tend to use both technologies. Yet, the effects of cloud computing and software defined networking on business network security are unclear. This study addresses this crucial issue. In a business network that uses both technologies, we start by looking at security, namely distributed denial-of-service (DDoS) attack defensive methods. SDN technology may help organizations protect against DDoS assaults provided the defensive architecture is structured appropriately. To mitigate DDoS attacks, we offer a highly configurable network monitoring and flexible control framework. We present a dataset shift-resistant graphic model-based attack detection system for the new architecture. The simulation findings demonstrate that our architecture can efficiently meet the security concerns of the new network paradigm and that our attack detection system can report numerous threats using real-world network data.
Trust Metric-Based Anomaly Detection via Deep Deterministic Policy Gradient R...IJCNCJournal
Addressing real-time network security issues is paramount due to the rapidly expanding IoT jargon. The erratic rise in usage of inadequately secured IoT- based sensory devices like wearables of mobile users, autonomous vehicles, smartphones and appliances by a larger user community is fuelling the need for a trustable, super-performant security framework. An efficient anomaly detection system would aim to address the anomaly detection problem by devising a competent attack detection model. This paper delves into the Deep Deterministic Policy Gradient (DDPG) approach, a promising Reinforcement Learning platform to combat noisy sensor samples which are instigated by alarming network attacks. The authors propose an enhanced DDPG approach based on trust metrics and belief networks, referred to as Deep Deterministic Policy Gradient Belief Network (DDPG-BN). This deep-learning-based approach is projected as an algorithm to provide “Deep-Defense” to the plethora of network attacks. Confidence interval is chosen as the trust metric to decide on the termination of sensor sample collection. Once an enlisted attack is detected, the collection of samples from the particular sensor will automatically cease. The evaluations and results of the experiments highlight a better detection accuracy of 98.37% compared to its counterpart conventional DDPG implementation of 97.46%. The paper also covers the work based on a contemporary Deep Reinforcement Learning (DRL) algorithm, the Actor Critic (AC). The proposed deep learning binary classification model is validated using the NSL-KDD dataset and the performance is compared to a few deep learning implementations as well.
Trust Metric-Based Anomaly Detection Via Deep Deterministic Policy Gradient R...IJCNCJournal
Addressing real-time network security issues is paramount due to the rapidly expanding IoT jargon. The erratic rise in usage of inadequately secured IoT- based sensory devices like wearables of mobile users, autonomous vehicles, smartphones and appliances by a larger user community is fuelling the need for a trustable, super-performant security framework. An efficient anomaly detection system would aim to address the anomaly detection problem by devising a competent attack detection model. This paper delves into the Deep Deterministic Policy Gradient (DDPG) approach, a promising Reinforcement Learning platform to combat noisy sensor samples which are instigated by alarming network attacks. The authors propose an enhanced DDPG approach based on trust metrics and belief networks, referred to as Deep Deterministic Policy Gradient Belief Network (DDPG-BN). This deep-learning-based approach is projected as an algorithm to provide “Deep-Defense” to the plethora of network attacks. Confidence interval is chosen as the trust metric to decide on the termination of sensor sample collection. Once an enlisted attack is detected, the collection of samples from the particular sensor will automatically cease. The evaluations and results of the experiments highlight a better detection accuracy of 98.37% compared to its counterpart conventional DDPG implementation of 97.46%. The paper also covers the work based on a contemporary Deep Reinforcement Learning (DRL) algorithm, the Actor Critic (AC). The proposed deep learning binary classification model is validated using the NSL-KDD dataset and the performance is compared to a few deep learning implementations as well.
A Comparative Study of Deep Learning Approaches for Network Intrusion Detecti...IRJET Journal
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Experimental analysis of intrusion detection systems using machine learning a...IJECEIAES
Since the invention of the internet for military and academic research purposes, it has evolved to meet the demands of the increasing number of users on the network, who have their scope beyond military and academics. As the scope of the network expanded maintaining its security became a matter of increasing importance. With various users and interconnections of more diversified networks, the internet needs to be maintained as securely as possible for the transmission of sensitive information to be one hundred per cent safe; several anomalies may intrude on private networks. Several research works have been released around network security and this research seeks to add to the already existing body of knowledge by expounding on these attacks, proffering efficient measures to detect network intrusions, and introducing an ensemble classifier: a combination of 3 different machine learning algorithms. An ensemble classifier is used for detecting remote to local (R2L) attacks, which showed the lowest level of accuracy when the network dataset is tested using single machine learning models but the ensemble classifier gives an overall efficiency of 99.8%.
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3rd International Conference on Artificial Intelligence Advances (AIAD 2024) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area advanced Artificial Intelligence. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the research area. Core areas of AI and advanced multi-disciplinary and its applications will be covered during the conferences.
As the Supervisory Control and Data Acquisition (SCADA) system are deployed in infrastructures which are critical to the survival of a nation, they have emerged as a potential terrain for cyber-war, thus attracting the considered attention of ‘nation-states’. The analysis of worms like ‘stuxnet’ ‘flame’ and ‘duqu’ reveals the hand of a ‘nation-state’ in their design and deployment. Hence, the necessity to understand various issues in the defence of SCADA systems arises. The forensics of the SCADA system provide deep insight into the design and deployment of the worm (the malware) once the system is attacked. This is precisely the scope of this essay.
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The future power system will be an innovative administration of existing power grids, which is called smart grid. Above all, the application of advanced communication and computing tools is going to significantly improve the productivity and consistency of smart grid systems with renewable energy resources. Together with the topographies of the smart grid, cyber security appears as a serious concern since a huge number of automatic devices are linked through communication networks. Cyber attacks on those devices had a direct influence on the reliability of extensive infrastructure of the power system. In this survey, several published works related to smart grid system vulnerabilities, potential intentional attacks, and suggested countermeasures for these threats have been investigated.
AN EFFICIENT SECURE CRYPTOGRAPHY SCHEME FOR NEW ML-BASED RPL ROUTING PROTOCOL...IJNSA Journal
Internet of Things (IoT) offers reliable and seamless communication for the heterogeneous dynamic lowpower and lossy network (LLNs). To perform effective routing in IoT communication, LLN Routing Protocol (RPL) is developed for the tiny nodes to establish connection by using deflaut objective functions: OF0, MRHOF, for which resources are constraints like battery power, computation capacity, memory communication link impacts on varying traffic scenarios in terms of QoS metrics like packet delivery ratio, delay, secure communication channel. At present, conventional Internet of Things (IoT) are having secure communication channels issue for transmission of data between nodes. To withstand those issues, it is necessary to balance resource constraints of nodes in the network. In this paper, we developed a security algorithm for IoT networks with RPL routing. Initially, the constructed network in corporates optimizationbased deep learning (reinforcement learning) for route establishment in IoT. Upon the establishment of the route, the ClonQlearn based security algorithm is implemented for improving security which is based onaECC scheme for encryption and decryption of data. The proposed security technique incorporates reinforcement learning-based ClonQlearnintegrated with ECC (ClonQlearn+ECC) for random key generation. The proposed ClonQlearn+ECCexhibits secure data transmission with improved network performance when compared with the earlier works in simulation. The performance of network expressed that the proposed ClonQlearn+ECC increased the PDR of approximately 8% - 10%, throughput of 7% - 13%, end-to-end delay of 5% - 10% and power consumption variation of 3% - 7%.
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This document summarizes a research paper that proposes a design for a secure, Wi-Fi integrated electricity meter called an Impregnable Device for Secured Metering (IDSM). The IDSM consists of a sophisticated meter with additional security features compared to traditional meters. It uses Wi-Fi communication, a microcontroller, and a centralized monitoring and control unit. Random number addressing cryptography (RAC) is chosen as the most secure encryption technique. The meter in each home connects via a wireless network to a server that calculates billing amounts and sends updates to be displayed on the home meter, reducing labor while increasing transparency. The design aims to provide secure communication at high speeds with an advanced metering system and unique database backend.
Practical analysis of the cybersecurity of European smart gridsSergey Gordeychik
This paper summarizes the experience gained during a series of
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smart electrical grids.
SECURITY AND PRIVACY AWARE PROGRAMMING MODEL FOR IOT APPLICATIONS IN CLOUD EN...ijccsa
This document summarizes a research paper on privacy-preserving techniques for IoT data in cloud environments. It introduces two differential privacy algorithms: 1) Generic differential privacy (GenDP) which provides generalized privacy protection for homogeneous and heterogeneous IoT metadata through data portioning. 2) Cluster-based differential privacy which groups similar data into clusters before defining classifiers to validate privacy. The paper evaluates these techniques and finds the cluster-based approach offers better security than customized interactive algorithms while maintaining data utility. Overall, the study presents new differential privacy methods for anonymizing IoT metadata stored in the cloud.
Detecting network attacks model based on a convolutional neural network IJECEIAES
Due to the increasing use of networks at present, Internet systems have raised many security problems, and statistics indicate that the rate of attacks or intrusions has increased excessively annually, and in the event of any malicious attack on network vulnerabilities or information systems, it may lead to serious disasters, violating policies on network security, i.e., “confidentiality, integrity, and availability” (CIA). Therefore, many detection systems, such as the intrusion detection system, appeared. In this paper, we built a system that detects network attacks using the latest machine learning algorithms and a convolutional neural network based on a dataset of the CSE-CIC-IDS2018. It is a recent dataset that contains a set of common and recent attacks. The detection rate is 99.7%, distinguishing between aggressive attacks and natural assertiveness.
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In networks, the rapidly changing traffic patterns of search engines, Internet of Things (IoT) devices, Big Data and data centers has thrown up new challenges for legacy; existing networks; and prompted the need for a more intelligent and innovative way to dynamically manage traffic and allocate limited network resources. Software Defined Network (SDN) which decouples the control plane from the data plane through network vitalizations aims to address these challenges. This paper has explored the SDN architecture and its implementation with the OpenFlow protocol. It has also assessed some of its benefits over traditional network architectures, security concerns and how it can be addressed in future research and related works in emerging economies such as Nigeria.
Intrusion Detection for HealthCare Network using Machine LearningIRJET Journal
1) The document discusses using machine learning techniques for intrusion detection in healthcare networks. It aims to build an effective intrusion detection system that can efficiently detect intrusions and provide safety for sensitive patient health information and medical data.
2) The methodology involves pre-processing the NSL-KDD dataset, training a decision tree classifier model, and using the trained model to predict intrusions. Accuracy of 90.3% was achieved using cross-validation.
3) Future work could include using all dataset features, immediately alerting administrators of attacks, and making the system multi-lingual. The system aims to provide secure access of healthcare data for authorized users and detect unauthorized access attempts.
Automated diagnosis of attacks in internet of things using machine learning a...journalBEEI
The Internet of Things (IoT) is the interconnection of things around us to make our daily process more efficient by providing more comfort and productivity. However, these connections also reveal a lot of sensitive data. Therefore, thinking about the methods of information security and coding are important as the security approaches that rely heavily on coding are not a strong match for these restricted devices. Consequently, this research aims to contribute to filling this gap, which adopts machine learning techniques to enhance network-level security in the low-power devices that use the lightweight MQTT protocol for their work. This study used a set of tools tools and, through various techniques, trained the proposed system ranging from Ensemble methods to deep learning models. The system has come to know what type of attack has occurred, which helps protect IoT devices. The log loss of the Ensemble methods is 0.44, and the accuracy of multi-class classification is 98.72% after converting the table data into an image set. The work also uses a Convolution Neural Network, which has a log loss of 0.019 and an accuracy of 99.3%. It also aims to implement these functions in IDS.
Recent changes to the existing power grid are expected to influence the way energy is provided and consumed by customers. Advanced Metering Infrastructure (AMI) is a tool to incorporate these changes for modernizing the electricity grid. Growing energy needs are forcing government agencies and utility companies to move towards AMI systems as part of larger smart grid initiatives. The smart grid promises to enable a more reliable, sustainable, and efficient power grid by taking advantage of information and communication technologies. However, this information-based power grid can reveal sensitive private information from the user’s perspective due to its ability to gather highly granular power consumption data. This has resulted in limited consumer acceptance and proliferation of the smart grid. Hence, it is crucial to design a mechanism to prevent the leakage of such sensitive consumer usage information in
smart grid. Among different solutions for preserving consumer privacy in Smart Grid Network(SGN), private data aggregation techniques have received a tremendous focus from security researchers. Existing privacy-preserving aggregation mechanisms in SGNs utilize cryptographic techniques, specifically homomorphic properties of public-key cryptosystems. Such homomorphic approaches are bandwidthintensive (due to large output blocks they generate), and in most cases, are computationally complex. In this paper, we present a novel and efficient CDMA-based approach to achieve privacy-preserving aggregation in SGNs by utilizing random perturbation of power consumption data and with limited use of traditional cryptography. We evaluate and validate the efficiency and performance of our proposed privacy preserving data aggregation scheme through extensive statistical analyses and simulations.
Machine learning-based intrusion detection system for detecting web attacksIAESIJAI
The increasing use of smart devices results in a huge amount of data, which raises concerns about personal data, including health data and financial data. This data circulates on the network and can encounter network traffic at any time. This traffic can either be normal traffic or an intrusion created by hackers with the aim of injecting abnormal traffic into the network. Firewalls and traditional intrusion detection systems detect attacks based on signature patterns. However, this is not sufficient to detect advanced or unknown attacks. To detect different types of unknown attacks, the use of intelligent techniques is essential. In this paper, we analyse some machine learning techniques proposed in recent years. In this study, several classifications were made to detect anomalous behaviour in network traffic. The models were built and evaluated based on the Canadian Institute for Cybersecurity-intrusion detection systems dataset released in 2017 (CIC-IDS-2017), which includes both current and historical attacks. The experiments were conducted using decision tree, random forest, logistic regression, gaussian naïve bayes, adaptive boosting, and their ensemble approach. The models were evaluated using various evaluation metrics such as accuracy, precision, recall, F1-score, false positive rate, receiver operating characteristic curve, and calibration curve.
Cyber-Defensive Architecture for Networked Industrial Control SystemsIJEACS
This paper deals with the inevitable consequence of the convenience and efficiency we benefit from the open, networked control system operation of safety-critical applications: vulnerability to such system from cyber-attacks. Even with numerous metrics and methods for intrusion detection and mitigation strategy, a complete detection and deterrence of internal code flaws and outside cyber-attacks has not been found and would not be found anytime soon. Considering the ever incompleteness of detection and prevention and the impact and consequence of mal-functions of the safety-critical operations caused by cyber incidents, this paper proposes a new computer control system architecture which assures resiliency even under compromised situations. The proposed architecture is centered on diversification of hardware systems and unidirectional communication from the proposed system in alerting suspicious activities to upper layers. This paper details the architectural structure of the proposed cyber defensive computer control system architecture for power substation applications and its validation in lab experimentation and on a cybersecurity testbed.
This document proposes using Software Defined Networking (SDN) to improve security in Internet of Things (IoT) networks. It discusses how SDN allows centralized control and programmability that can be used to implement security applications and dynamically enforce security policies. The document presents a framework that uses an SDN controller and edge node running virtual machines. It collects network flow data and uses an anomaly detection algorithm to identify malicious flows based on variance from expected values. When anomalies are detected, security policies are applied through the SDN controller to mitigate the threats, such as rate limiting or blocking malicious traffic flows. Simulation results show the effectiveness of the anomaly detection algorithm improves as the time window size increases.
Security and risk analysis in the cloud with software defined networking arch...IJECEIAES
Cloud computing has emerged as the actual trend in business information technology service models, since it provides processing that is both costeffective and scalable. Enterprise networks are adopting software-defined networking (SDN) for network management flexibility and lower operating costs. Information technology (IT) services for enterprises tend to use both technologies. Yet, the effects of cloud computing and software defined networking on business network security are unclear. This study addresses this crucial issue. In a business network that uses both technologies, we start by looking at security, namely distributed denial-of-service (DDoS) attack defensive methods. SDN technology may help organizations protect against DDoS assaults provided the defensive architecture is structured appropriately. To mitigate DDoS attacks, we offer a highly configurable network monitoring and flexible control framework. We present a dataset shift-resistant graphic model-based attack detection system for the new architecture. The simulation findings demonstrate that our architecture can efficiently meet the security concerns of the new network paradigm and that our attack detection system can report numerous threats using real-world network data.
Trust Metric-Based Anomaly Detection via Deep Deterministic Policy Gradient R...IJCNCJournal
Addressing real-time network security issues is paramount due to the rapidly expanding IoT jargon. The erratic rise in usage of inadequately secured IoT- based sensory devices like wearables of mobile users, autonomous vehicles, smartphones and appliances by a larger user community is fuelling the need for a trustable, super-performant security framework. An efficient anomaly detection system would aim to address the anomaly detection problem by devising a competent attack detection model. This paper delves into the Deep Deterministic Policy Gradient (DDPG) approach, a promising Reinforcement Learning platform to combat noisy sensor samples which are instigated by alarming network attacks. The authors propose an enhanced DDPG approach based on trust metrics and belief networks, referred to as Deep Deterministic Policy Gradient Belief Network (DDPG-BN). This deep-learning-based approach is projected as an algorithm to provide “Deep-Defense” to the plethora of network attacks. Confidence interval is chosen as the trust metric to decide on the termination of sensor sample collection. Once an enlisted attack is detected, the collection of samples from the particular sensor will automatically cease. The evaluations and results of the experiments highlight a better detection accuracy of 98.37% compared to its counterpart conventional DDPG implementation of 97.46%. The paper also covers the work based on a contemporary Deep Reinforcement Learning (DRL) algorithm, the Actor Critic (AC). The proposed deep learning binary classification model is validated using the NSL-KDD dataset and the performance is compared to a few deep learning implementations as well.
Trust Metric-Based Anomaly Detection Via Deep Deterministic Policy Gradient R...IJCNCJournal
Addressing real-time network security issues is paramount due to the rapidly expanding IoT jargon. The erratic rise in usage of inadequately secured IoT- based sensory devices like wearables of mobile users, autonomous vehicles, smartphones and appliances by a larger user community is fuelling the need for a trustable, super-performant security framework. An efficient anomaly detection system would aim to address the anomaly detection problem by devising a competent attack detection model. This paper delves into the Deep Deterministic Policy Gradient (DDPG) approach, a promising Reinforcement Learning platform to combat noisy sensor samples which are instigated by alarming network attacks. The authors propose an enhanced DDPG approach based on trust metrics and belief networks, referred to as Deep Deterministic Policy Gradient Belief Network (DDPG-BN). This deep-learning-based approach is projected as an algorithm to provide “Deep-Defense” to the plethora of network attacks. Confidence interval is chosen as the trust metric to decide on the termination of sensor sample collection. Once an enlisted attack is detected, the collection of samples from the particular sensor will automatically cease. The evaluations and results of the experiments highlight a better detection accuracy of 98.37% compared to its counterpart conventional DDPG implementation of 97.46%. The paper also covers the work based on a contemporary Deep Reinforcement Learning (DRL) algorithm, the Actor Critic (AC). The proposed deep learning binary classification model is validated using the NSL-KDD dataset and the performance is compared to a few deep learning implementations as well.
A Comparative Study of Deep Learning Approaches for Network Intrusion Detecti...IRJET Journal
This document presents a comparative study of deep learning approaches for network intrusion detection. It employs deep neural networks to predict attacks on network intrusion detection systems using the KDD Cup-99 dataset. A DNN with 3 layers demonstrated superior performance compared to other machine learning algorithms and DNNs with varying layers. The study finds that deep learning techniques can function at a superhuman level when combined with intrusion detection systems due to their ability to adapt to new data and detect novel attacks.
Experimental analysis of intrusion detection systems using machine learning a...IJECEIAES
Since the invention of the internet for military and academic research purposes, it has evolved to meet the demands of the increasing number of users on the network, who have their scope beyond military and academics. As the scope of the network expanded maintaining its security became a matter of increasing importance. With various users and interconnections of more diversified networks, the internet needs to be maintained as securely as possible for the transmission of sensitive information to be one hundred per cent safe; several anomalies may intrude on private networks. Several research works have been released around network security and this research seeks to add to the already existing body of knowledge by expounding on these attacks, proffering efficient measures to detect network intrusions, and introducing an ensemble classifier: a combination of 3 different machine learning algorithms. An ensemble classifier is used for detecting remote to local (R2L) attacks, which showed the lowest level of accuracy when the network dataset is tested using single machine learning models but the ensemble classifier gives an overall efficiency of 99.8%.
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DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
1. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.15, No.3, May 2024
DOI:10.5121/ijaia.2024.15301 1
DEEP LEARNING FOR SMART GRID INTRUSION
DETECTION: A HYBRID CNN-LSTM-BASED MODEL
Abdulhakim Alsaiari and Mohammad Ilyas
Department of Electrical Engineering and Computer Science, Florida Atlantic
University, Boca Raton, FL, USA
ABSTRACT
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
KEYWORDS
Security, Smart Grid, SCADA, DNP3, Intrusion Detection & Deep Learning.
1. INTRODUCTION
Traditionally, the term "grid" indicates the infrastructure that supports four essential electricity
functions within the realm of electricity: power generation, long-distance transmission,
distribution, and consumption [1]. The traditional power grid system (TG) is outdated, and it’s
no longer capable of meeting the growing demand for electricity. It’s limited when using
distributed and renewable energy sources, and it’s also inefficient when dealing with faults and
issues. Hence, there is a shared motivation across academia and industry to upgrade to a smart
power grid that aligns with contemporary living standards [2]. The smart grid is considered one
of the most prominent applications of the Internet of Things (IoT). It consists of two
infrastructures, known as the power infrastructure for electricity flow and the communication
infrastructure for information flow [3]. These grids are capable of transmitting power from
generating stations to consumers and information from consumers to generating stations.
Moreover, the term "Smart Grid" (SG) refers to the next generation of power grids that integrate
Information and Communication Technologies (ICT) [4]. The implementation of these
technologies enhances the efficiency and reliability of monitoring and regulating the generation,
distribution, and consumption of electrical energy [5]. However, with the integration of
information and communication infrastructure, modern power grids have become more
susceptible to various cyberattacks. Therefore, ensuring the timely and precise identification of
potential threats is important, particularly in the context of industrial operations and smart grids,
2. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.15, No.3, May 2024
2
to effectively address and minimize risks. Thus, deep learning techniques are commonly used in
such smart grid systems to protect SCADA systems.
The use of Supervisory Control and Data Acquisition systems (SCADA) in critical infrastructure
sectors is extensive. They contribute efficiently to monitoring and controlling industrial
processes, enhancing operational efficiency, maintaining safety and regulatory compliance, and
optimizing resource utilization. SCADA systems are widely employed across various industries,
including energy, water and wastewater management, transportation, manufacturing, and more.
In the context of the smart grid, SCADA is essential for enabling the smart grid to operate
efficiently, reliably, and securely [6]. It provides real-time monitoring, control, and management
capabilities across the electrical grid. Thus, securing the SCADA system from cyber threats is
considered a serious concern. Another issue to consider is the lack of security features in many
ICS/SCADA-oriented protocols like DNP3, widely used in manufacturing processes and
particularly in the utility and energy sectors, including smart grids. Therefore, integrating
technologies into ICS/SCADA systems for protection commonly involves utilizing machine
learning and deep learning to develop an Intrusion Detection System (IDS) [7]. These
technologies aim to enhance the security of ICS/SCADA systems and protect them against
cyberattacks. Currently, there is a growing trend in the utilization of deep learning algorithms for
the purpose of intrusion detection in a SCADA-based smart grid. Moreover, securing the smart
grid from cyber threats is considered a serious concern [8]. Therefore, the implementation of
IDSs has significantly enhanced the efficiency of these intelligent infrastructures by detecting
potential security threats and mitigating their risks [9].
An Intrusion Detection System (IDS) is designed to monitor network traffic and enhance the
security level by promptly identifying and potentially mitigating security threats. The
implementation of IDS is crucial in securing essential networks from the rising challenges
caused by malicious activities. The three components that comprise an IDS architecture are
agents, an analysis engine, and a response module. An IDS can include several agents to monitor
and capture the network activities of one or more systems. Furthermore, the analysis engine
component initiates an investigation into potential cyberattacks. Eventually, when the response
module detects a possible security breach, it notifies the system administrator or security team
[10]. Additionally, one feature of the analysis engine is its ability to incorporate several methods
for detecting cyberattacks. Signature-based and anomaly-based are the two main types of these
mechanisms [11]. Signature-based intrusion detection mostly employs a blacklist approach,
which is limited to identifying unknown attacks, and it’s necessary to update the attack detection
library continuously [12]. On the other hand, the objective of the anomaly-based IDS is to detect
atypical behavioral patterns via the comparison of characteristics between normal and abnormal
activities. An anomaly-based approach is distinguished by its capacity to identify novel forms of
attacks and exhibit reduced long-term costs while maintaining a high level of resilience to
changes in the environment [11]. Typically, this mechanism incorporates techniques derived
from machine learning and deep learning, including decision trees, Artificial Neural Networks
(ANN), and clustering algorithms [10]. Researchers have conducted extensive research in the
field of intrusion detection systems to develop advanced IDSs [13]. Furthermore, the utilization
of intrusion detection technology is a highly efficient method for ensuring the security of a
network. Additionally, an intrusion detection system can be created by utilizing a hybrid
methodology that merges a feature selection model with a proficient classification technique [6].
Smart grids emerged as a result of the integration of digital technology into power systems. This
revolution has raised security concerns, especially regarding the communication networks that
support these systems. The main purpose of this research paper is to develop an efficient hybrid
DL model to enhance the performance of smart grid IDS. This paper's contribution can be briefly
stated as follows:
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• To exploit the power of DL, we proposed a hybrid DL model that combines CNN and
LSTM for smart grid IDS.
• For training and testing our model for binary-class classification, we employed an
intrusion detection dataset, namely DNP3, focusing on unauthorized commands and DoS
cyberattacks against the IEC 60870-5-104 protocol, which is commonly used in smart grid
SCADA systems.
• Furthermore, numerous comprehensive experiments were executed, including
hyperparameter tuning, to verify the effectiveness of the proposed technique for IDS in
smart grids.
• The findings of our proposed approach show significant improvements across key
performance metrics such as accuracy, precision, recall, and F1 score, with an accuracy
rate of 99.50% and a detection rate of nearly 100%.
This paper is organized as follows: Section 2 provides a literature review of related studies.
Section 3 describes the background of deep learning architecture. Section 4 introduces the
proposed CNN-LSTM deep learning model and describes the applied dataset. In Section 5, the
performance evaluation of the model and the result analysis of the experiment are illustrated.
Finally, the conclusion and future work are described in Section 6.
2. RELATED STUDIES
The use of technology is rapidly advancing in today’s organizations. However, the IoT networks
have a certain number of weaknesses in the field; one of them is the scope of security itself. New
technologies must improve the ability to find breaches on the Internet of Things network. Based
on the nature of the input data, the authors of [14] implemented the current solution using
convolutional neural networks (CNNs), which generally offer an appropriate and effective deep
learning method for processing inputs, especially with large numbers of dimensions that a
standard neural network would be ill-equipped to handle. [15] implemented a multi-scale
convolutional neural network (CNN) to automatically classify anatomical MR brain images into
several groups. The approach achieves accurate segmentation details while preserving spatial
consistency thanks to the use of a multi-scale methodology. The research paper [16] proposes a
CNN model that uses binary and multiclass classification for detecting anomalies. The CSE-
CICIDS 2018 dataset includes Advanced Denial of Service (DoS) attacks, including those
targeting the application layer.
Researchers in [17] combined convolutional neural networks (CNNs) and recurrent gated units
(RGUs) to evaluate network traffic patterns and identify anomalous behaviors indicative of
DDoS assaults. Their studies' findings demonstrate how well the suggested hybrid deep-learning
system performs, as evidenced by its 99.86% accuracy rate in identifying DDoS assaults. Abu
Bakar et al. have expanded on earlier work by proposing and designing an IDS system. They
have also tested the suggested architecture under a range of malicious instances, including DDoS
assaults and floods. The suggested technique, fully dispersed, sounds like an early warning siren
when pre-attack actions use network traffic.
The paper [18] provides a new decentralized method that uses Federated Learning (FL) to
identify anomalies in smart grids with a precision that is equivalent to conventional techniques.
To detect unusual usage of power in smart grids, they created FL models. To increase the breadth
of smart grid recognition, the authors of [19] developed a local detection approach based on
CNNs and GRUs, installed it in many isolated branch nodes, and extracted the important stream
data using the technique of attention. For DDoS assaults in the cyber-physical combination of the
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smart grid, [20] suggests an approach fused with a convolutional neural network (CNN) and a
gated recurrent unit (GRU).
[1] offers an anomaly detection approach that can identify abnormalities and categorize
anomalies into specific incident types. The suggested MENSA model integrates two deep neural
networks (DNNs) at the same time: an autoencoder and a generative adversarial network (GAN).
The model provided by [21] applies initial condensed hidden layers to extract the necessary
details from the input data and rebuild the supplied network sample. Using the chosen ideal
threshold, it can accurately identify both known and unknown harm. A CNN-LSTM model was
developed by [23] for smart grid data categorization to calculate the omitted cases in the dataset
using the local quantities about the missing data point, a unique data preliminary processing
technique.
The authors of [27] developed an efficient approach for reproducing the input data at the output
layer with minimal reconstruction error. When anomalies arise, the trained model has a high
error rate and is unable to recreate aberrant occurrences. The system uses mistakes as an
indication to distinguish between typical and anomalous situations. To minimize harm to vital
infrastructure, the research paper [45] suggests a computerized, multifaceted alerting method for
identifying abnormalities in SCADA networks. An approach for the complex identification of
anomalies and reliable gathering of features. The Gated Recurrent Units (GRU) deep learning
technique has been used in [2] to identify DDoS and intrusion attempts in their proposed SDN
defensive system, which depends on the examination of individual IP traffic records.
Accelerating mitigating actions using direct flow inspection reduces the effect of the attack on
the SDN.
The writers of [28] suggest using particle swarm optimization (PSO) to find FDIA in the SG
framework using convolutional neural networks with long short-term memory (CNN-LSTM). It
detects an anomalous monitoring value and identifies the kind of anomaly using phasor
measurement unit (PMU) readings. To detect islanding, the 1D CNN and CNN-LSTM models
are suggested by [41]. This study presents proactive islanding methods for recognizing
coordinated and inverter-based microgrids. To estimate the condition of the power system during
denial-of-service attacks, the researchers in [43] suggest a hybrid adjustment model based on
deep neural networks that also uses a self-regressive model to address the issue of neural system
models' scaling apathy.
The research paper [8] provides a framework that uses a convolutional neural network to create
an equilibrium between two inputs by combining scales of time-series data and network traffic
parameters. The suggested machine learning topology aids in the very precise detection ability of
their anomaly detector. Snort outperformed Suricata in terms of detection precision, as reported
by [333]. According to [39], cutting-edge machine learning (ML) algorithms can anticipate
harmful attack anomalies, which are then used to train security models and forecast any unusual
activity.
3. BACKGROUND
3.1. Deep Learning
Deep learning is a broader field of artificial intelligence that has recently exhibited substantial
achievements in several fields due to its capability to autonomously learn and make decisions.
Deep learning techniques are inspired by the neural networks of the human brain. It utilizes
complicated architectures consisting of multiple layers of interconnected nodes to independently
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learn complex patterns and characteristics in photos, text, audio, and other various data [17].
Moreover, deep learning algorithms autonomously identify complex relationships within
extensive input data, enabling the model to generate predictions or execute tasks without explicit
programming. As a result, deep learning has exhibited notable successes across various fields
such as cybersecurity, natural language processing, and many other autonomous systems [41].
Several deep learning architectures have recently emerged to tackle different tasks. Those
architectures employ multiple hidden layers between the input and output layers to extract more
advanced patterns and characteristics from vast amounts of data. Furthermore, deep learning
employs various architectures and algorithms to efficiently address various types of tasks by
identifying complex patterns in a large amount of data [39]. Notably, CNN and LSTM are the
most widely implemented deep learning architectures in a variety of applications in which the
objective to be predicted is explicitly annotated within the training data.
3.2. Convolutional Neural Network (CNN) for Intrusion Detection
Convolutional Neural Networks (CNNs) are specific types of artificial neural networks that are
inspired by the human visual cortex to recognize objects in real time. The main objective of
using CNNs is to automatically handle and analyze visual data, making the data useful in tasks
such as pattern and image recognition. In general, CNN is defined as a classic neural network
architecture where data flows hierarchically from input to output across a series of
interconnected nodes organized in layers. Convolutional, pooling, and fully-connected layers are
the three key components of building CNN architecture [23]. Due to the capability of CNNs to
automatically learn hierarchical representations from data, they have become a fundamental
aspect in many fields, such as intrusion detection systems (IDSs).
CNNs in intrusion detection can analyze patterns in network traffic and identify unusual
behaviors or potential security risks. The application of CNNs in vital infrastructures such as
smart grids exploits the power of deep learning to improve security aspects. While incorporating
information and communication technologies into traditional power grids has its benefits, there is
an increased risk of various cyber threats and attacks targeting smart grids [7]. The following are
several advantages of utilizing CNNs for smart grid intrusion detection:
• CNNs capability to handle heterogeneous and high-dimensional data makes them highly
suitable for the complex and varied data sources present in smart grids.
• Their capacity to handle substantial volumes of data allows for effective and immediate
analysis of network traffic, ensuring prompt identification and response to any security
breaches.
• CNNs have the capability to acquire knowledge and adjust to evolving attack strategies,
thereby enhancing their resilience against emerging threats.
• CNN-based IDS can decrease the occurrence of false positives by accurately
differentiating between normal network behaviors and malicious actions. This helps to
alleviate the workload of security staff.
• By autonomously acquiring features from the data, CNN-based intrusion detection
systems can decrease reliance on manual feature engineering, which is both time-
consuming and prone to errors [24].
3.3. Long Short-term Memory (LSTM) for Intrusion Detection
Long Short-Term Memory (LSTM) is a specific type of Recurrent Neural Network (RNN) that
was developed to capture long-term temporal dependencies and address the issue of vanishing
gradient difficulties. RNNs' concealed layers are substituted with LSTM units, which encompass
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memory cells and gates. The memory cells retain and store information under the control of the
gates. The input gate, output gate, and forget gate are utilized to regulate the influx or outflow of
information within the memory cell. Before adding new data to each memory cell, the LSTM
network's architecture allows for the historical data to be forgotten from each memory cell [41].
For instance, the forget gate determines, given the previous concealed state and the current input
data, which parts of the cell state are useful at time step t. The forget gate's cell state can have
irrelevant values removed by the LSTM network, while important values can be identified and
updated [25]. LSTMs are highly suitable for processing and analyzing sequential data, making
them especially useful in applications such as time series prediction, natural language processing,
speech recognition, and intrusion detection.
The Intrusion Detection System (IDS) is an essential tool that aims to ensure the availability,
confidentiality, and integrity of data [21]. In fact, deep learning models are effective in dynamic
and vast network environments due to their capacity to extract unique features without relying on
manually designed feature extractions. Consequently, many researchers in smart grids
concentrate on the advancement of IDS that are based on deep learning [22]. In addition, the
LSTM deep learning technique is exceptionally effective and resilient when applied to smart grid
intrusion detection systems [23]. LSTM demonstrates efficacy in capturing and evaluating
temporal relationships in the data, rendering it highly suitable for detecting abnormalities and
potential security breaches in the realm of smart grids. By exploiting the capabilities of LSTM,
the intrusion detection system can improve its capacity to identify and react to cyber threats in
the everchanging and dynamic environment of smart grids [24]. The following are some
advantages of adopting LSTM in smart grid intrusion detection:
• Long Short-Term Memory (LSTM) networks demonstrate proficiency in representing
temporal dependencies in data, enabling them to accurately capture the sequential patterns
and temporal relationships inherent in smart grid activities. The identification of potential
anomalies or intrusions that may occur over time is of utmost importance [14].
• Time series data, such as energy usage patterns and variations in the condition of the grid,
are frequently included in smart grid data. Thus, LSTM's ability to handle time-dependent
data makes it useful for identifying anomalous patterns or behaviors that could be signs of
an intrusion [25].
• The LSTM architecture incorporates memory cells capable of retaining and transmitting
substantial information from the early stages of the network to the final stage. This
functionality facilitates the network's capacity to preserve long-term dependencies within
Smart Grid data, hence improving its capability to identify tiny deviations or anomalies
[23].
• LSTM models are highly efficient in processing sequential data, enabling real-time
intrusion detection within smart grids. Quick identification and response to security threats
is crucial for avoiding any potential disruptions [26].
• In RNN models, improperly assigned weights can cause vanishing and expanding gradient
issues. LSTMs effectively address the common issue of vanishing gradients in RNNs,
facilitating more efficient training on complex smart grid data [27].
4. PROPOSED HYBRID MODEL
4.1. System Description
Figure 1 illustrates the proposed hybrid deep-learning model for the Intrusion Detection System
(IDS). The proposed hybrid model combines the Convolutional Neural Network model (CNN)
and the Long Short-Term Memory model (LSTM) to develop an advanced Deep Learning (DL)
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technique to detect various types of cyberattacks. CNN is chosen to be applied due to its ability
to capture position-invariant features. This position-invariant property is particularly valuable for
tasks where the precise location of a feature or object in an image may vary, such as object
recognition in computer vision, where objects can appear at different positions and orientations
in images [17]. On the other hand, LSTM is a special type of RNN that adds some specific gate
structures, including forgetting gates, input gates, and output gates. Furthermore, when compared
to RNN with a single gate, LSTM offers a more robust technique for retaining short-term
memories while also not losing long-term dependencies. In addition, designing an intrusion
detection system model that combines CNN and LSTM is a powerful approach that allows for
the extraction of both spatial features using CNNs and temporal dependencies using LSTMs
[28].
For the purpose of strengthening the network, the algorithm architecture is equipped with two
LSTM blocks and three CNN blocks. The convolution layer is responsible for extracting features
from the input data and generating a feature map. To capture the feature mapping, the
convolutional network multiplies the convolutional kernel by the input data, and then a non-
linear activation function is applied to the feature map. The weights and biases in the
convolutional kernel are initialized randomly [12]. Each CNN layer is followed by a max-
pooling layer. The max-pooling operation creates a down-sampled version of the input feature
map by selecting the maximum value from each feature within a certain area. In the
concatenation layer, the final flattened output of CNN and the output of LSTM are combined.
The concatenation layer is followed by a fully connected layer. The purpose of adding a dropout
layer after the fully connected layer is to avoid overfitting. To convert the output to a probability
distribution, the classification layer is linked to the SoftMax layer. This enables the classification
layer to generate precise predictions regarding the different types of labels [20]. Eventually, the
model is trained and tested using the DNP3 intrusion detection dataset.
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Figure 1. Proposed CNN–LSTM hybrid model
4.2. Dataset Description
Table 1. Samples for training and testing the model.
Type Total Samples Training Samples Test Samples Label
Normal 666 466 200 0
Attack 5,328 3,728 1600 1
DNP3 is widely utilized as a SCADA communication protocol, and its popularity has grown,
particularly in the context of smart grids. Thus, an intrusion detection dataset from
ITHACAUniversity of Western Macedonia (DNP3) [29] is applied to train and test our hybrid
DL model for smart grid IDS. This dataset is publicly available and contains a wide range of
normal and DNP3 attack scenarios that meet real-world criteria. Denial of Service (DoS) and
unauthorized DNP3 commands are the focus of these malicious attacks. Based on flow features
such as time stamps, source and destination IPs, source and destination ports, protocols, and
attacks, the network traffic analysis is generated and included in this dataset by utilizing
CICFlowMeter with labeled flows. Additionally, a custom DNP3 Python parser is also used for
parsing and analyzing DNP3 communication packets within Supervisory Control and Data
Acquisition (SCADA) and industrial control systems to ensure the security and reliability of
critical infrastructure. The dataset generated consisted of 40,420 network flows, each containing
99 features. There were a total of nine labels utilized, consisting of eight attack labels and one
normal flow label. Consequently, this dataset may be utilized to develop AI-powered Intrusion
Detection and Prevention (IDPS) systems based on ML and DL techniques.
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Data cleaning is the first step in preparing the dataset to be suitable for binary and multi-class
classification using machine learning and deep learning methods. The features are then converted
into numerical features and incorporated into the dataset along with any other numerical features.
Furthermore, the labels in the dataset are numerically encoded, with the label "Normal"
represented by 0 and the other DNP3 cyberattack labels such as “DNP3_ENUMERATE” and
STOP_APP represented by 1. To reduce the feature variations, the dataset was uniformly
normalized and mapped within the range of [0, 1]. The decision to consider all features and not
perform feature selection has been made due to the absence of irrelevant features in the dataset,
as shown in Figure 2, and based on the belief that each feature contributes valuable information
to the model. Furthermore, the model's decision-making process is influenced by all available
features in the dataset because omitting any might lead to a loss of relevant information or
compromise the model's performance.
A normalization procedure has been applied to the dataset to bring the numerical values of
different features onto a similar scale. In addition, the applicable dataset has a total of 99
columns with 5994 records. Each record is identified and labeled as an attack or not, where 1
represents the attack labels and 0 represents the normal labels. As illustrated in Table 1, the
dataset is split into a training set and a testing set with a ratio of 70:30. The model is trained
using 70% of the data, while the remaining 30% of the data is allocated for validation and testing
after the training is completed.
• The following are the components of the confusion matrix that are used to calculate
various performance metrics for the hybrid model classifier:
• True Positive (TP): This denotes a correct prediction by the algorithm when the instance is
classified as positive, and it’s truly positive.
• True Negative (TN): This denotes a correct prediction by the algorithm when the instance
is classified as negative, and it’s truly negative.
• False Positive (FP): This denotes a wrong prediction by the algorithm when the instance is
classified as positive, but it is negative.
• False Negative (FN): This denotes a wrong prediction by the algorithm when the instance
is classified as negative, but it’s positive.
The four metrics that are utilized to evaluate the hybrid model's performance are represented
mathematically as in [18, 20, 28, 17] and written in subsequent equations as follows:
𝑇𝑃 + 𝑇𝑁
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁
𝑇𝑃
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
𝑇𝑃 + 𝐹𝑃
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Figure 2. The dataset correlation map
5. EXPERIMENTS AND RESULTS
To develop a reliable intrusion detection model, it is necessary to preprocess the large dataset
first. After preparing the dataset, it has been applied to the proposed model and to different
standalone deep learning models, such as CNN and LSTM. The experiments were conducted in
the Python programming language on Jupyter Notebook, and Keras was employed as a deep
learning framework. I have executed many experiments with various hyperparameters to get
better results. The primary goal of hyperparameter tuning was to enhance the performance of the
deep neural network on the chosen dataset.
The optimal selection of hyperparameters is crucial for building a successful neural network
architecture, as the performance of the trained model depends on these values. Therefore, we
must consider various hyperparameters when initiating effective deep learning classifiers for
intrusion detection systems. Thus, I have assessed the model's performance by carefully varying
hyperparameter values such as batch size, epochs, and learning rate. By applying the
hyperparameter values illustrated in Table 2, I have successfully enhanced the performance of
our intrusion detection model. After conducting various experiments, I realized that the learning
rate clearly affects the performance of deep learning models. Even though a high learning rate
can lead to faster convergence during training, it's more likely to be susceptible to the risk of
overshooting the optimal solution. Thus, Adam (Adaptive Moment Estimation) was utilized as
an adaptive learning rate optimizer. Moreover, a learning rate of 0.0001 consistently delivers
strong performance, prevents overshooting, and improves model performance across different
datasets. It is also critical to select the optimal number of epochs to effectively train a deep
learning model. Overfitting can occur when the model learns noises and exhibits poor
performance when applied to unseen data. We prevented underfitting and overfitting by applying
epochs between 50 and 100 during our training process. An increased batch size provides the
potential to capture a greater number of global patterns, which can speed up the training process.
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However, a large patch size increases computational complexity and memory demands. As a
result, a patch size in the range of 16 and 32 was appropriate for our model and resulted in a high
rate of accuracy. In addition, with experimentation with different dropout rates, I figured out that
a dropout rate of 0.5 is the optimal value for our model and dataset, and it can help correct
overfitting. When dealing with datasets that contain complex patterns, it’s recommended to
increase the number of convolutional layers, which results in improved performance. However,
larger convolutional layers often include more parameters and computations, resulting in higher
computational costs and memory consumption during training and inference. Hence, to achieve
the best results, we propose employing three convolutional layers. We employed 64 and 128
LSTM units in our model, considering factors like model performance, computational efficiency,
and potential overfitting.
Table 2. Hyperparameters for our model.
Parameter Value
Learning rate 0.0001
Epoch 50 to 100
Batch size 16 to 32
Optimizer Adam
Dropout rate 0.5
Convolutional layers 3
LSTM units 64, 128
Figure 3. illustrates the performance of the proposed approach compared to existing algorithms
in terms of accuracy, precision, recall, and f1-score. I have assessed the model's performance by
carefully varying hyperparameter values such as batch size, epochs, and learning rate. By
applying the hyperparameter values illustrated in Table 2, I have successfully enhanced the
performance of our intrusion detection model. The results demonstrate that the algorithm we
suggest achieves high levels of accuracy, precision, recall, and f1-score, specifically 99.50%,
99.51%, 99.93%, and 99.72%, respectively. The CNN exhibits an accuracy of 99.33%, a
precision of 99.50%, a recall of 99.75%, and a f1-score of 99.62%. On the other hand, the LSTM
model achieved 94.39% accuracy, a precision of 93.82%, a recall of 99.94%, and a f1-score of
96.72%. Noticeably, the proposed CNN-LSTM hybrid model demonstrated superior
performance compared to the comparison algorithms in all categories, except for the recall
category. The CNNLSTM algorithm's recall dropped because of the increased FN value
compared to the LSTM algorithm, which plays a crucial role. For clarification, the CNN-LSTM
algorithm achieved an FN percentage of 0.18, while the LSTM achieved an FN percentage of
0.06. In terms of intrusion detection, the proposed algorithm provides distinctive performance
compared to the existing algorithms (see Table 3).
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Figure 3. Performance Comparison of the considered algorithms
Table 3. Comparison with relevant works.
The validation loss metric is used to assess how well the model performs on unseen data during
training. In this context, a lower validation loss indicates better performance, as it means the
model is making more accurate predictions on new data. The proposed CNN-LSTM algorithm
achieved its best validation performance, with a validation loss of 0.0155, at the 39th epoch. The
CNN algorithm performed better than the LSTM algorithm, achieving a validation loss of 0.0172
at the 39th epoch, while the LSTM algorithm achieved its best validation performance, with a
loss of 0.1660 at the 49th epoch (see Figures. 4, 5, and 6). Overall, lower validation loss values
indicate better model performance when generating predictions on new data, and the epoch at
which these low validation loss values are obtained indicates when the model is at its most
accurate. Additionally, the comparison with CNN and LSTM algorithms demonstrates the
superiority of the CNN-LSTM hybrid architecture in generalizing unseen data, as it
outperformed both standalone CNN and LSTM models in terms of validation loss.
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Figure 4. Validation Performance for the proposed CNN-LSTM
Figure 5. Validation Performance for the CNN
Figure 6. Validation Performance for the LSTM
6. CONCLUSION AND FUTURE WORK
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Securing smart grid communication networks is crucial since electricity systems are becoming
more digital. Smart grids that rely on SCADA are capable of gathering and controlling data in
real-time because of Distributed Network Protocol 3 (DNP3) and other protocols. Robust
intrusion detection systems are essential for early threat identification and mitigation since these
connected networks are vulnerable to different cyberattacks. In this paper, a hybrid DL model
specifically designed for intrusion detection in smart grids is proposed to address the problem of
protecting smart grid communication networks. By combining the CNN and LSTM algorithms,
we have developed a deep learning model for detecting intrusions in smart grid systems. The
model was trained and tested using a recent intrusion detection dataset that focused on DNP3.
Therefore, our proposed approach performed effectively in detecting DNP3 unauthorized
commands and DoS cyberattacks on the DNP3 Intrusion Detection dataset. In addition, our
hybrid CNN-LSTM model performed well in detecting and classifying intrusions, achieving high
performance rates across various metrics. Exploiting the power of deep learning, the model
obtained a high accuracy rate of 99.50%, precision of 99.51%, recall of 99.93%, and F1score of
99.72%. Furthermore, superior generalization was exhibited by our model on unseen data,
reflected by low validation loss values. Overall, the result of our study demonstrates the
effectiveness of using deep learning approaches, particularly hybrid architectures, for IDS in
complex smart grid environments.
Our future work will focus on enhancing and examining the proposed model's performance by
combining various DL techniques and expanding the performance analysis. Moreover, different
intrusion detection datasets will be applied to our DL model, such as IEC 60870-5-104, which
contains cyberattack activities against the IEC 60870-5-104 communication protocol, which is
widely used in smart grid SCADA. In addition, we aim to improve the model's performance and
reduce the manual effort required for hyperparameter tuning. This is achieved by applying
hyperparameter optimization techniques such as Bayesian optimization, genetic algorithms, or
reinforcement learning-based approaches. Eventually, several strategies and techniques will be
investigated to enhance computational and memory consumption without compromising the
accuracy and effectiveness of our intrusion detection model.
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