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Delivering User Behavior Analytics at Apache Hadoop Scale : A new perspective on cybersecurity with Securonix 


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Cloudera, Inc.
Cloudera, Inc.

Learn how to: * Detect threats automatically and accurately
 * Reduce threat response times from 7 days to 4 hour
 * Ingest and process 100+TB per day for automated machine learning and behavior-based detection


Software
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1© Cloudera, Inc. All rights reserved. | Delivering User Behavior Analytics at Apache Hadoop Scale A New Perspective on Cybersecurity with Securonix & Cloudera |
2© Cloudera, Inc. All rights reserved. | Agenda • Introduction • Industry Overview • SNYPR: Big data enabled security analytics • Q&A
3© Cloudera, Inc. All rights reserved. | Today’s Speakers Tanuj Gulati Co-Founder and Chief Technology Officer Rocky DeStefano Cybersecurity Subject Matter Expert
4© Cloudera, Inc. All rights reserved. | Industry Overview
5© Cloudera, Inc. All rights reserved. | Security Operations Modernized Security Architecture Security Analysts Find Advanced Threats Faster Security Responders Rapid Investigation Over any Timeline Benefits of Apache Hadoop for Cybersecurity
6© Cloudera, Inc. All rights reserved. | Legacy Cyber Solutions (TBs) Aggregated Events Raw System Logs Network Flows/ DNS Full Packet Capture Video, Text, Images User Data Data Types (MBs>PBs) Search Correlations SQL Machine Learning Advanced Statistics 1 10 20 40 Time (Months) 3 Cloudera’s Hadoop Based Cybersecurity Solutions (PB) • Gartner named Cloudera Non-Security-Specific Analytics Vendors to Watch1 • 60% of UEBA Vendors to Watch use CDH1 • 25% of Network Traffic Analysis Vendors to Watch use CDH1 • 50% of MSSP ‘Leaders’ use CDH2 Modernizing the Cybersecurity Architecture Security Operations 1 Market Trends: User and Entity Behavior Analytics (UEBA) Expand Their Market Reach – Gartner April 2016 2 Magic Quadrant for Managed Security Services, Worldwide – Gartner December 2015
7© Cloudera, Inc. All rights reserved. | Find Advanced Threats Faster Technical Indicators Context Anomaly Detection Behavior Analytics Sentiment Analysis Operational InsightMachine Learning OPERATIONS Cloudera Manager Cloudera Director DATA MANAGEMENT Cloudera Navigator Encrypt and KeyTrustee Optimizer STRUCTURED Sqoop UNSTRUCTURED Kafka, Flume PROCESS, ANALYZE, SERVE UNIFIED SERVICES RESOURCE MANAGEMENT YARN SECURITY Sentry, RecordService STORE INTEGRATE BATCH Spark, Hive, Pig MapReduce STREAM Spark SQL Impala SEARCH Solr OTHER Kite NoSQL HBase OTHER Object Store FILESYSTEM HDFS RELATIONAL Kudu Security Analysts 623 940 379
8© Cloudera, Inc. All rights reserved. | Why User Behavior Analytics? Network FileEndpoint Context ContextUser Complete Enterprise Visibility Logs User Behavior Analytics Enables: Incident Detection: •Data Exfiltration •Privileged Account Misuse •Sabotage •Account Takeover •Lateral Movement Operational Insight: •Cleanup Rogue Access Privileges •Access Reviews •Access Certifications Incident Context: • Understand true Source and the User / Entity Impact to the business Endpoint Logs Applications File Context
9© Cloudera, Inc. All rights reserved. | SNYPR: Big Data enabled security analytics
© 2016 / Confidential 10 The Anatomy of a Basic Cyber Attack Agenda Current State of Security Monitoring Next Generation Security Monitoring SNYPR: Big Data Enabled Security Analytics SNYPR: Success Stories & Demonstration
© 2016 / Confidential 11 The Anatomy of a Basic Cyber Attack Kill Chain Threat Indicators Composite Threats Phishing Malicious content Account Compromise System Compromise Data Consumption Data EgressMalware Infection Data Exfiltration Lateral MovementDrive-by Download Basic APT Spear Phishing 10 M Emails 40 M Website Visits 200 M Processes 400 M Netflow 3 M Data Egresses To detect this basic cyber attack, organizations must analyze … per day: Phishing Attempt Malicious Content Compromised Endpoint Lateral Movement Data Exfiltration
© 2016 / Confidential 12 Current State - Enterprise Security Monitoring • Proprietary data store(s) • Information siloes • Partial context • Expensive data retention • Signature based threat detection • Too Many Alerts • High number of false positives • Correlation across small time window • Threat centric • Requires multiple systems • Reactive & post-attack • External ticketing system THREATSRESPONSE MONITORING • Limited Entity Context • Weak Visualization • Hours to Search • Days to Investigate DATA REPOSITORY
© 2016 / Confidential 13 Next Generation Security Analytics  Open Data Model  Massively Scalable  Very High Ingestion Rate  Long Term Storage BIG DATA PLATFORM
© 2016 / Confidential 14 Next Generation Security Analytics LOG MANAGEMENT  Collection & Normalization  Entity Attribution  Context Enrichment  Text Indexing  Open Data Model  Massively Scalable  Very High Ingestion Rate  Long Term Storage BIG DATA PLATFORM
© 2016 / Confidential 15 Next Generation Security Analytics INVESTIGATION & RESPONSE  Cross Device Event Correlation  Behavior Based Anomaly Detection  Entity Centric Risk Scoring  Threat Models ADVANCED ANALYTICS LOG MANAGEMENT  Collection & Normalization  Entity Attribution  Context Enrichment  Text Indexing  Open Data Model  Massively Scalable  Very High Ingestion Rate  Long Term Storage BIG DATA PLATFORM
© 2016 / Confidential 16 Next Generation Security Analytics  Investigation Workbench  Search & Visualization Palette  Data Link Analysis  Case Management & Workflows  Privacy Controls INVESTIGATION & RESPONSE  Cross Device Event Correlation  Behavior Based Anomaly Detection  Entity Centric Risk Scoring  Threat Models ADVANCED ANALYTICS LOG MANAGEMENT  Collection & Normalization  Entity Attribution  Context Enrichment  Text Indexing  Open Data Model  Massively Scalable  Very High Ingestion Rate  Long Term Storage BIG DATA PLATFORM
© 2016 / Confidential 17 Securonix SNYPR – Next Gen Security Analytics SECURITY DATA LAKE DETECTION  Hunt @ Speed of Thought  Super-enriched Events  On-Demand Visualization  Scale to Petabytes  Open Data Model  Ingest @ 1,200,000+ EPS  Normalize > Correlate > Store  Real Time and Batched Analytics  Machine Learning  Predictive Analytics  Behavior & Signature Based  Threat Model Based Alerts  Entity Centric  Investigate Data Linkages  Integrated Threat Management
© 2016 / Confidential 18 What is Securonix SNYPR? • UEBA (User and Entity Behavior Analytics) + Next Gen Security Event Management (SIEM) + Fraud Analytics - on one platform • SNYPR is a Big Data based, Machine Learning platform with out of box threat and risk detection models for Insider Threat, Cyber Threat and Fraud • Ingests and analyzes security event logs, network flows and application transactions from hundreds of sources
© 2016 / Confidential 19 Recent Patents 1. Behavior Anomaly Detection for Identification of Malicious Activity 2. Anomaly Detection Using Adaptive Behavioral Profiles 3. Risk Scoring in Behavioral Analysis Award Winning Technology Certified
© 2016 / Confidential 20 • Long Term Data Retention • Text Indexing • Correlation Rules Engine • Behavior Anomaly Engine • Peer Anomaly Engine • Event Rarity Engine • DGA and Beaconing Detection • Threat Models HBASE Super Enrichment K A F K A SPARK STREAMING SERVICES • In-memory normalization, attribution & analytics • Distributed and parallelized processing Prioritized Threats Monitor & Search Investigation & Response RAW HDFS SOLR ENRICHED HOSTS Windows/Unix/Mainframe COMMUNCIATION eMail/Chat/Phone PERIMETER IDS/IDP/Firewall/VPN MALWARE Sandboxing/Antivirus NETWORK Netflow/Pcap/ VLAN ACL CLOUD IAAS. PAAS, SAAS ENTERPRISE APPS SAP / OFS / EPIC / CERNER ANALYTICS SNYPR - How does it work? DATA STORAGE INGESTIONNODE IDENTITY HRMS / IAM THREAT INTEL OPEN / COTS
© 2016 / Confidential 21 Entity Correlation & Enrichment >> “Context” Behavior Profiling Event RarityPeer Group Profiling “Purpose-Built” Analytics Digitally Generated AlgorithmsRobotic Patterns (Beaconing) Repeated machine like pattern Connection attempts to suspiciously formed domains
© 2016 / Confidential 22 •Data Exfiltration •Privileged Account Misuse •Sabotage •Snooping •Reconnaissance •Malicious Traffic •Lateral Movement •Account Takeover •Malicious Process •DNS Traffic •Cloud Application Misuse •Infrastructure Sabotage •Retail Fraud •Banking Fraud •ATM Transactions •Trade Surveillance •Manufacturing Fraud INSIDER THREAT CYBER THREAT CLOUD SECURITY FRAUD IDENTITY & ACCESS •Cleanup Rogue Access Privileges •Access Reviews •Access Certifications •Access Requests Our Packaged Applications
© 2016 / Confidential 23  Use Case: Endpoint Protection (POS Terminals)  Data Sources: HRMS Data, POS Events, Netflow  Value Proposition:  Automated correlation of all events to the endpoint  Analyze all endpoint generated data to detect: • Suspicious Process execution • Abnormal Network Flows • Rare File Md5 hashes detected • Suspicious Lateral Movements  Use Case: Insider Threat Protection  Data Sources: HR App, Proxy, Mail Gateway, Hosts, Badging App, Travel App, Network DLP, Confidential Apps  Value Proposition:  Correlate all events to the user identity  Analyze all user generated events to detect: • Unauthorized Data Access: Confidential data / network attempt or accessed than allowed by clearance levels • Data Exfiltration Attempts: Abnormally high volume or frequency of data egressed  Use Case: Patient Health Record Protection  Data Sources: EPIC, Cerner, Medicity and other clinical apps  Value Proposition:  Automated correlation of all PHR access attempts to appropriate staff members  Analyze all PHR Data Access Attempts to detect: • Unauthorized PHR Access: Non Physician staff members accessing PHR records • VIP Snooping: Abnormally high volume of access attempts on a single PHR data • Neighbor snooping: Closely located Employee and Patient • Family Snooping – Co-located Employee and Patient Customer Profile: Large Healthcare Client, TX Customer Profile: Top 3 Financial Institution  Use Case: Privileged Account Protection  Data Sources: Hosts, Databases, VPN, Privileged Identity Mgmt., DLP Events, Web Gateway, Mail Gateway  Value Proposition:  Analyze activities performed by Privileged accounts for misuse • Suspected Malware: Phishing attempt, Visit to malware infected sites, Suspicious process running on host, Infected Files detected on system • Suspected Data Exfiltration: High volume of data transfers • Suspected Sabotage attempt: Suspicious commands run on host Customer Profile: Top 5 Federal Contractor Customer Profile: Top 3 Retailer Customer Success Examples
© 2016 / Confidential 24 Key Takeaways • Maturity/Market Leadership • Tried & Tested Technology • Out of the box ‘Threat Models’ • Privacy Features approved by EMEA workers councils • Scalable & Fault Tolerant • Hadoop Enabled Application • Time to Value/Lower Cost of Ownership • 300+ Connectors • 99.6% True Positive Rate • Threat Exchange • 40+ Contributors • Connectors + Threat Models Solution Maturity Ease of Integration OOB Threat Models Scalable Architecture Securonix Value Proposition
© 2016 / Confidential 25 Demonstration - SNYPR The Big Data Security Analytics Platform
© 2016 / Confidential 26 Welcome to SNYPR
© 2016 / Confidential 27 HIGH-RISK ENTITIES Dashboard
© 2016 / Confidential 28 INVESTIGATE THREAT Violations
© 2016 / Confidential 29 INVESTIGATE THREAT Data Link Analysis
© 2016 / Confidential 30 INVESTIGATE THREAT Location Analysis
© 2016 / Confidential 31 MANAGE THREAT Disposition
© 2016 / Confidential 32 SEARCH @ SPEED OF THOUGHT Hunting for Threats
© 2016 / Confidential 33 SEARCH @ SPEED OF THOUGHT Super Enriched Search
© 2016 / Confidential 34 SEARCH @ SPEED OF THOUGHT 2D Bar Chart – Drill Down
© 2016 / Confidential 35 SEARCH @ SPEED OF THOUGHT 3D – Stacked Bar Chart
© 2016 / Confidential 36 SEARCH @ SPEED OF THOUGHT Custom Dashboards and Visualization
37© Cloudera, Inc. All rights reserved. | Interested in learning more?
38© Cloudera, Inc. All rights reserved. | Contact our experts Schedule a discovery session with our experts Discuss how Securonix and Cloudera can work with you Tanuj Gulati tgulati@securonix.com Rocky DeStefano rocky@cloudera.com

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Delivering User Behavior Analytics at Apache Hadoop Scale : A new perspective on cybersecurity with Securonix 


  • 1. 1© Cloudera, Inc. All rights reserved. | Delivering User Behavior Analytics at Apache Hadoop Scale A New Perspective on Cybersecurity with Securonix & Cloudera |
  • 2. 2© Cloudera, Inc. All rights reserved. | Agenda • Introduction • Industry Overview • SNYPR: Big data enabled security analytics • Q&A
  • 3. 3© Cloudera, Inc. All rights reserved. | Today’s Speakers Tanuj Gulati Co-Founder and Chief Technology Officer Rocky DeStefano Cybersecurity Subject Matter Expert
  • 4. 4© Cloudera, Inc. All rights reserved. | Industry Overview
  • 5. 5© Cloudera, Inc. All rights reserved. | Security Operations Modernized Security Architecture Security Analysts Find Advanced Threats Faster Security Responders Rapid Investigation Over any Timeline Benefits of Apache Hadoop for Cybersecurity
  • 6. 6© Cloudera, Inc. All rights reserved. | Legacy Cyber Solutions (TBs) Aggregated Events Raw System Logs Network Flows/ DNS Full Packet Capture Video, Text, Images User Data Data Types (MBs>PBs) Search Correlations SQL Machine Learning Advanced Statistics 1 10 20 40 Time (Months) 3 Cloudera’s Hadoop Based Cybersecurity Solutions (PB) • Gartner named Cloudera Non-Security-Specific Analytics Vendors to Watch1 • 60% of UEBA Vendors to Watch use CDH1 • 25% of Network Traffic Analysis Vendors to Watch use CDH1 • 50% of MSSP ‘Leaders’ use CDH2 Modernizing the Cybersecurity Architecture Security Operations 1 Market Trends: User and Entity Behavior Analytics (UEBA) Expand Their Market Reach – Gartner April 2016 2 Magic Quadrant for Managed Security Services, Worldwide – Gartner December 2015
  • 7. 7© Cloudera, Inc. All rights reserved. | Find Advanced Threats Faster Technical Indicators Context Anomaly Detection Behavior Analytics Sentiment Analysis Operational InsightMachine Learning OPERATIONS Cloudera Manager Cloudera Director DATA MANAGEMENT Cloudera Navigator Encrypt and KeyTrustee Optimizer STRUCTURED Sqoop UNSTRUCTURED Kafka, Flume PROCESS, ANALYZE, SERVE UNIFIED SERVICES RESOURCE MANAGEMENT YARN SECURITY Sentry, RecordService STORE INTEGRATE BATCH Spark, Hive, Pig MapReduce STREAM Spark SQL Impala SEARCH Solr OTHER Kite NoSQL HBase OTHER Object Store FILESYSTEM HDFS RELATIONAL Kudu Security Analysts 623 940 379
  • 8. 8© Cloudera, Inc. All rights reserved. | Why User Behavior Analytics? Network FileEndpoint Context ContextUser Complete Enterprise Visibility Logs User Behavior Analytics Enables: Incident Detection: •Data Exfiltration •Privileged Account Misuse •Sabotage •Account Takeover •Lateral Movement Operational Insight: •Cleanup Rogue Access Privileges •Access Reviews •Access Certifications Incident Context: • Understand true Source and the User / Entity Impact to the business Endpoint Logs Applications File Context
  • 9. 9© Cloudera, Inc. All rights reserved. | SNYPR: Big Data enabled security analytics
  • 10. © 2016 / Confidential 10 The Anatomy of a Basic Cyber Attack Agenda Current State of Security Monitoring Next Generation Security Monitoring SNYPR: Big Data Enabled Security Analytics SNYPR: Success Stories & Demonstration
  • 11. © 2016 / Confidential 11 The Anatomy of a Basic Cyber Attack Kill Chain Threat Indicators Composite Threats Phishing Malicious content Account Compromise System Compromise Data Consumption Data EgressMalware Infection Data Exfiltration Lateral MovementDrive-by Download Basic APT Spear Phishing 10 M Emails 40 M Website Visits 200 M Processes 400 M Netflow 3 M Data Egresses To detect this basic cyber attack, organizations must analyze … per day: Phishing Attempt Malicious Content Compromised Endpoint Lateral Movement Data Exfiltration
  • 12. © 2016 / Confidential 12 Current State - Enterprise Security Monitoring • Proprietary data store(s) • Information siloes • Partial context • Expensive data retention • Signature based threat detection • Too Many Alerts • High number of false positives • Correlation across small time window • Threat centric • Requires multiple systems • Reactive & post-attack • External ticketing system THREATSRESPONSE MONITORING • Limited Entity Context • Weak Visualization • Hours to Search • Days to Investigate DATA REPOSITORY
  • 13. © 2016 / Confidential 13 Next Generation Security Analytics  Open Data Model  Massively Scalable  Very High Ingestion Rate  Long Term Storage BIG DATA PLATFORM
  • 14. © 2016 / Confidential 14 Next Generation Security Analytics LOG MANAGEMENT  Collection & Normalization  Entity Attribution  Context Enrichment  Text Indexing  Open Data Model  Massively Scalable  Very High Ingestion Rate  Long Term Storage BIG DATA PLATFORM
  • 15. © 2016 / Confidential 15 Next Generation Security Analytics INVESTIGATION & RESPONSE  Cross Device Event Correlation  Behavior Based Anomaly Detection  Entity Centric Risk Scoring  Threat Models ADVANCED ANALYTICS LOG MANAGEMENT  Collection & Normalization  Entity Attribution  Context Enrichment  Text Indexing  Open Data Model  Massively Scalable  Very High Ingestion Rate  Long Term Storage BIG DATA PLATFORM
  • 16. © 2016 / Confidential 16 Next Generation Security Analytics  Investigation Workbench  Search & Visualization Palette  Data Link Analysis  Case Management & Workflows  Privacy Controls INVESTIGATION & RESPONSE  Cross Device Event Correlation  Behavior Based Anomaly Detection  Entity Centric Risk Scoring  Threat Models ADVANCED ANALYTICS LOG MANAGEMENT  Collection & Normalization  Entity Attribution  Context Enrichment  Text Indexing  Open Data Model  Massively Scalable  Very High Ingestion Rate  Long Term Storage BIG DATA PLATFORM
  • 17. © 2016 / Confidential 17 Securonix SNYPR – Next Gen Security Analytics SECURITY DATA LAKE DETECTION  Hunt @ Speed of Thought  Super-enriched Events  On-Demand Visualization  Scale to Petabytes  Open Data Model  Ingest @ 1,200,000+ EPS  Normalize > Correlate > Store  Real Time and Batched Analytics  Machine Learning  Predictive Analytics  Behavior & Signature Based  Threat Model Based Alerts  Entity Centric  Investigate Data Linkages  Integrated Threat Management
  • 18. © 2016 / Confidential 18 What is Securonix SNYPR? • UEBA (User and Entity Behavior Analytics) + Next Gen Security Event Management (SIEM) + Fraud Analytics - on one platform • SNYPR is a Big Data based, Machine Learning platform with out of box threat and risk detection models for Insider Threat, Cyber Threat and Fraud • Ingests and analyzes security event logs, network flows and application transactions from hundreds of sources
  • 19. © 2016 / Confidential 19 Recent Patents 1. Behavior Anomaly Detection for Identification of Malicious Activity 2. Anomaly Detection Using Adaptive Behavioral Profiles 3. Risk Scoring in Behavioral Analysis Award Winning Technology Certified
  • 20. © 2016 / Confidential 20 • Long Term Data Retention • Text Indexing • Correlation Rules Engine • Behavior Anomaly Engine • Peer Anomaly Engine • Event Rarity Engine • DGA and Beaconing Detection • Threat Models HBASE Super Enrichment K A F K A SPARK STREAMING SERVICES • In-memory normalization, attribution & analytics • Distributed and parallelized processing Prioritized Threats Monitor & Search Investigation & Response RAW HDFS SOLR ENRICHED HOSTS Windows/Unix/Mainframe COMMUNCIATION eMail/Chat/Phone PERIMETER IDS/IDP/Firewall/VPN MALWARE Sandboxing/Antivirus NETWORK Netflow/Pcap/ VLAN ACL CLOUD IAAS. PAAS, SAAS ENTERPRISE APPS SAP / OFS / EPIC / CERNER ANALYTICS SNYPR - How does it work? DATA STORAGE INGESTIONNODE IDENTITY HRMS / IAM THREAT INTEL OPEN / COTS
  • 21. © 2016 / Confidential 21 Entity Correlation & Enrichment >> “Context” Behavior Profiling Event RarityPeer Group Profiling “Purpose-Built” Analytics Digitally Generated AlgorithmsRobotic Patterns (Beaconing) Repeated machine like pattern Connection attempts to suspiciously formed domains
  • 22. © 2016 / Confidential 22 •Data Exfiltration •Privileged Account Misuse •Sabotage •Snooping •Reconnaissance •Malicious Traffic •Lateral Movement •Account Takeover •Malicious Process •DNS Traffic •Cloud Application Misuse •Infrastructure Sabotage •Retail Fraud •Banking Fraud •ATM Transactions •Trade Surveillance •Manufacturing Fraud INSIDER THREAT CYBER THREAT CLOUD SECURITY FRAUD IDENTITY & ACCESS •Cleanup Rogue Access Privileges •Access Reviews •Access Certifications •Access Requests Our Packaged Applications
  • 23. © 2016 / Confidential 23  Use Case: Endpoint Protection (POS Terminals)  Data Sources: HRMS Data, POS Events, Netflow  Value Proposition:  Automated correlation of all events to the endpoint  Analyze all endpoint generated data to detect: • Suspicious Process execution • Abnormal Network Flows • Rare File Md5 hashes detected • Suspicious Lateral Movements  Use Case: Insider Threat Protection  Data Sources: HR App, Proxy, Mail Gateway, Hosts, Badging App, Travel App, Network DLP, Confidential Apps  Value Proposition:  Correlate all events to the user identity  Analyze all user generated events to detect: • Unauthorized Data Access: Confidential data / network attempt or accessed than allowed by clearance levels • Data Exfiltration Attempts: Abnormally high volume or frequency of data egressed  Use Case: Patient Health Record Protection  Data Sources: EPIC, Cerner, Medicity and other clinical apps  Value Proposition:  Automated correlation of all PHR access attempts to appropriate staff members  Analyze all PHR Data Access Attempts to detect: • Unauthorized PHR Access: Non Physician staff members accessing PHR records • VIP Snooping: Abnormally high volume of access attempts on a single PHR data • Neighbor snooping: Closely located Employee and Patient • Family Snooping – Co-located Employee and Patient Customer Profile: Large Healthcare Client, TX Customer Profile: Top 3 Financial Institution  Use Case: Privileged Account Protection  Data Sources: Hosts, Databases, VPN, Privileged Identity Mgmt., DLP Events, Web Gateway, Mail Gateway  Value Proposition:  Analyze activities performed by Privileged accounts for misuse • Suspected Malware: Phishing attempt, Visit to malware infected sites, Suspicious process running on host, Infected Files detected on system • Suspected Data Exfiltration: High volume of data transfers • Suspected Sabotage attempt: Suspicious commands run on host Customer Profile: Top 5 Federal Contractor Customer Profile: Top 3 Retailer Customer Success Examples
  • 24. © 2016 / Confidential 24 Key Takeaways • Maturity/Market Leadership • Tried & Tested Technology • Out of the box ‘Threat Models’ • Privacy Features approved by EMEA workers councils • Scalable & Fault Tolerant • Hadoop Enabled Application • Time to Value/Lower Cost of Ownership • 300+ Connectors • 99.6% True Positive Rate • Threat Exchange • 40+ Contributors • Connectors + Threat Models Solution Maturity Ease of Integration OOB Threat Models Scalable Architecture Securonix Value Proposition
  • 25. © 2016 / Confidential 25 Demonstration - SNYPR The Big Data Security Analytics Platform
  • 26. © 2016 / Confidential 26 Welcome to SNYPR
  • 27. © 2016 / Confidential 27 HIGH-RISK ENTITIES Dashboard
  • 28. © 2016 / Confidential 28 INVESTIGATE THREAT Violations
  • 29. © 2016 / Confidential 29 INVESTIGATE THREAT Data Link Analysis
  • 30. © 2016 / Confidential 30 INVESTIGATE THREAT Location Analysis
  • 31. © 2016 / Confidential 31 MANAGE THREAT Disposition
  • 32. © 2016 / Confidential 32 SEARCH @ SPEED OF THOUGHT Hunting for Threats
  • 33. © 2016 / Confidential 33 SEARCH @ SPEED OF THOUGHT Super Enriched Search
  • 34. © 2016 / Confidential 34 SEARCH @ SPEED OF THOUGHT 2D Bar Chart – Drill Down
  • 35. © 2016 / Confidential 35 SEARCH @ SPEED OF THOUGHT 3D – Stacked Bar Chart
  • 36. © 2016 / Confidential 36 SEARCH @ SPEED OF THOUGHT Custom Dashboards and Visualization
  • 37. 37© Cloudera, Inc. All rights reserved. | Interested in learning more?
  • 38. 38© Cloudera, Inc. All rights reserved. | Contact our experts Schedule a discovery session with our experts Discuss how Securonix and Cloudera can work with you Tanuj Gulati tgulati@securonix.com Rocky DeStefano rocky@cloudera.com

Editor's Notes

  1. Good morning, good afternoon, and good evening. Thank you for joining us for today’s webinar: Delivering User Behavior Analytics at Apache Haddop Scape, A New Perspective on Cybersecurity with Securonix and Cloudera.
  2. Today’s webinar will have three sections: A higher level industry overview, followed by a deeper dive into SNYPER. A Q&A portion will take place toward the end of today’s webinar. To ask a question, just type it in the chat box at the lower left corner of your window and submit. NEXT SLIDE
  3. Your speaker’s for today’s webinar are Rocky DeStefano, Cloudera’s Cybersecurity Subject Matter Expert, and Tanuj Gulati, Co-Founder and Chief Technology Officer of Securonix. I will now turn it over to Rocky.
  4. CDH
  5. A starting point on files: PDF EXE JAVA PCAP PE Files RAR ZIP SWF MS Office RTF MHTML YARA Mach-O XOR
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