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Jisheng Wang at AI Frontiers: Deep Learning in Security

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AI Frontiers
AI Frontiers

Deep learning is the next wave of AI-based attack detection. We will share our customer-driven experiences and learnings from building a comprehensive User and Entity Behavior Analytics (UEBA) solution using Apache Spark and Google Tensorflow to detect multi-stage advanced attacks. We will also discuss the challenges and guidelines for successfully deploying deep learning in broader security.

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Deep Learning In Security: An Empirical Example in User & Entity Behavior Analytics (UEBA) Jisheng Wang, Min-Yi Shen
2© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential  Jisheng Wang, Chief Scientist in Niara • Over 12-year experiences of applying machine learning and big data technology to security • Ph.D from Penn State – ML in security with 100GB data • Technical Leader in Cisco – Security Intelligence Operations (SIO) with 10B/day • Lead the overall big data analytics innovation and development in Niara  Niara • Recognized leader by Gartner in user and entity behavior analytics (UEBA) • Re-invent enterprise security analytics for attack detection and incident response ME, US
3© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential USER & ENTITY BEHAVIOR ANALYTICS UEBA SECURITY why this matters UEBA SOLUTION how to detect attacks before damage is done BEYOND DEEP LEARNING how to build a comprehensive solution YOU ARE HERE
4© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential PROBLEM THE SECURITY GAP PREVENTION & DETECTION (US $B) SECURITY SPEND # BREACHES DATA BREACHES
5© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential PROBLEM CAUSE OF THE GAP ATTACKERS ARE QUICKLY INNOVATING & ADAPTING BATTLEFIELD WITH IOT AND CLOUD, SECURITY IS BORDERLESS
6© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential PROBLEM ADDRESSING THE CAUSE ATTACKERS ARE QUICKLY INNOVATING & ADAPTING DEEP LEARNING SOLUTIONS MUST BE RESPONSIVE TO CHANGES
7© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential PROBLEM ADDRESSING THE CAUSE BATTLEFIELD WITH IOT AND CLOUD, SECURITY IS BORDERLESS INSIDER BEHAVIOR LOOK AT BEHAVIOR CHANGE OF INSIDE USERS AND MACHINES
8© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential USER & ENTITY BEHAVIOR ANALYTICS (UEBA) MACHINE LEARNING DRIVEN BEHAVIOR ANALYTICS IS A NEW WAY TO COMBAT ATTACKERS 1 2 3 Machine driven, not only human driven Detect compromised users, not only attackers Post-infection detection, not only prevention
9© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential REAL WORLD NEWS WORTHY EXAMPLES COMPROMISED 40 million credit cards were stolen from Target’s severs STOLEN CREDENTIALS NEGLIGENT DDoS attack from 10M+ hacked home devices took down major websites ALL USED THE SAME PASSWORD MALICIOUS Edward Snowden stole more than 1.7 million classified documents INTENDED TO LEAK INFORMATION
10© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential USER & ENTITY BEHAVIOR ANALYTICS UEBA SECURITY why this matters UEBA SOLUTION how to detect attacks before damage is done BEYOND DEEP LEARNING how to build a comprehensive solution YOU ARE HERE
11© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential REAL WORLD ATTACKS CAUGHT BY NIARA SCANNING ATTACK scan servers in the data center to find out vulnerable targets DETECTED WITH AD LOGS EXFILTRATION OF DATA upload a large file to cloud server hosted in new country never accessed before DETECTED WITH WEB PROXY LOGS DATA DOWNLOAD download data from internal document repository which is not typical for the host DETECTED WITH NETWORK TRAFFIC
12© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential BEHAVIOR ENCODING – USER User 1 User 2
13© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential BEHAVIOR ENCODING – USER VS MACHINE User Machine
14© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential BEHAVIOR ANOMALY USER | EXFILTRATION User – Before Compromise User – Post Compromise
15© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential BEHAVIOR ANOMALY MACHINE | DATA DOWNLOAD Dropcam – Before Compromise Dropcam – Post Compromise
16© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential BEHAVIOR DETECTION ARCHITECTURE Stream Data Pre-processing Behavior Encoding Input Data User Activities Labeled User Behavior Repository Apache Spark Behavior Anomaly Detection CNN Training Behavior Classifier Tensorflow
17© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential CNN – COMPUTATION GRAPH Behavior Image (24x60x9 ) 8x20 Convolution User Labels Feature Maps (24x60x40) Feature Maps (12x30x40) Feature Maps (12x30x80) Feature Maps (6x15x80) Output Layer 1024 Nodes 2x2 Pooling 4x10 Convolution 2x2 Pooling Fully Connected Fully Connected with Dropout Feature Extraction Classification
18© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential CNN – PROGRESSION OF TRAINING ERROR TrainingError # of minibatches (100 profiles/batch)
19© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential USER & ENTITY BEHAVIOR ANALYTICS UEBA SECURITY what is UEBA UEBA SOLUTION infrastructure needed to deep learning BEYOND DEEP LEARNING how to build a comprehensive solution YOU ARE HERE
20© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential BEYOND DEEP LEARNING ENSEMBLE LEARNING Behavioral Analytics Internal Resource Access Finance servers Authentication AD logins Remote Access VPN logins External Activity C&C, personal email SaaS Activity Office 365, Box Cloud IaaS AWS, Azure Physical Access badge logs Exfiltration DLP, Email Ensemble approach using a mix of different models over various types of behaviors from the same entity
21© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential BEYOND DEEP LEARNING REINFORCEMENT LEARNING Models Alerts User Feedback Interactive Learning Local Context Input Data Self Learning Initial Parameters
22© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential USER & ENTITY BEHAVIOR ANALYTICS UEBA SECURITY what is UEBA UEBA SOLUTION infrastructure needed to deep learning BEYOND DEEP LEARNING how to build a comprehensive solution
Thank You

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Jisheng Wang at AI Frontiers: Deep Learning in Security

  • 1. Deep Learning In Security: An Empirical Example in User & Entity Behavior Analytics (UEBA) Jisheng Wang, Min-Yi Shen
  • 2. 2© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential  Jisheng Wang, Chief Scientist in Niara • Over 12-year experiences of applying machine learning and big data technology to security • Ph.D from Penn State – ML in security with 100GB data • Technical Leader in Cisco – Security Intelligence Operations (SIO) with 10B/day • Lead the overall big data analytics innovation and development in Niara  Niara • Recognized leader by Gartner in user and entity behavior analytics (UEBA) • Re-invent enterprise security analytics for attack detection and incident response ME, US
  • 3. 3© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential USER & ENTITY BEHAVIOR ANALYTICS UEBA SECURITY why this matters UEBA SOLUTION how to detect attacks before damage is done BEYOND DEEP LEARNING how to build a comprehensive solution YOU ARE HERE
  • 4. 4© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential PROBLEM THE SECURITY GAP PREVENTION & DETECTION (US $B) SECURITY SPEND # BREACHES DATA BREACHES
  • 5. 5© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential PROBLEM CAUSE OF THE GAP ATTACKERS ARE QUICKLY INNOVATING & ADAPTING BATTLEFIELD WITH IOT AND CLOUD, SECURITY IS BORDERLESS
  • 6. 6© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential PROBLEM ADDRESSING THE CAUSE ATTACKERS ARE QUICKLY INNOVATING & ADAPTING DEEP LEARNING SOLUTIONS MUST BE RESPONSIVE TO CHANGES
  • 7. 7© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential PROBLEM ADDRESSING THE CAUSE BATTLEFIELD WITH IOT AND CLOUD, SECURITY IS BORDERLESS INSIDER BEHAVIOR LOOK AT BEHAVIOR CHANGE OF INSIDE USERS AND MACHINES
  • 8. 8© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential USER & ENTITY BEHAVIOR ANALYTICS (UEBA) MACHINE LEARNING DRIVEN BEHAVIOR ANALYTICS IS A NEW WAY TO COMBAT ATTACKERS 1 2 3 Machine driven, not only human driven Detect compromised users, not only attackers Post-infection detection, not only prevention
  • 9. 9© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential REAL WORLD NEWS WORTHY EXAMPLES COMPROMISED 40 million credit cards were stolen from Target’s severs STOLEN CREDENTIALS NEGLIGENT DDoS attack from 10M+ hacked home devices took down major websites ALL USED THE SAME PASSWORD MALICIOUS Edward Snowden stole more than 1.7 million classified documents INTENDED TO LEAK INFORMATION
  • 10. 10© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential USER & ENTITY BEHAVIOR ANALYTICS UEBA SECURITY why this matters UEBA SOLUTION how to detect attacks before damage is done BEYOND DEEP LEARNING how to build a comprehensive solution YOU ARE HERE
  • 11. 11© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential REAL WORLD ATTACKS CAUGHT BY NIARA SCANNING ATTACK scan servers in the data center to find out vulnerable targets DETECTED WITH AD LOGS EXFILTRATION OF DATA upload a large file to cloud server hosted in new country never accessed before DETECTED WITH WEB PROXY LOGS DATA DOWNLOAD download data from internal document repository which is not typical for the host DETECTED WITH NETWORK TRAFFIC
  • 12. 12© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential BEHAVIOR ENCODING – USER User 1 User 2
  • 13. 13© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential BEHAVIOR ENCODING – USER VS MACHINE User Machine
  • 14. 14© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential BEHAVIOR ANOMALY USER | EXFILTRATION User – Before Compromise User – Post Compromise
  • 15. 15© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential BEHAVIOR ANOMALY MACHINE | DATA DOWNLOAD Dropcam – Before Compromise Dropcam – Post Compromise
  • 16. 16© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential BEHAVIOR DETECTION ARCHITECTURE Stream Data Pre-processing Behavior Encoding Input Data User Activities Labeled User Behavior Repository Apache Spark Behavior Anomaly Detection CNN Training Behavior Classifier Tensorflow
  • 17. 17© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential CNN – COMPUTATION GRAPH Behavior Image (24x60x9 ) 8x20 Convolution User Labels Feature Maps (24x60x40) Feature Maps (12x30x40) Feature Maps (12x30x80) Feature Maps (6x15x80) Output Layer 1024 Nodes 2x2 Pooling 4x10 Convolution 2x2 Pooling Fully Connected Fully Connected with Dropout Feature Extraction Classification
  • 18. 18© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential CNN – PROGRESSION OF TRAINING ERROR TrainingError # of minibatches (100 profiles/batch)
  • 19. 19© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential USER & ENTITY BEHAVIOR ANALYTICS UEBA SECURITY what is UEBA UEBA SOLUTION infrastructure needed to deep learning BEYOND DEEP LEARNING how to build a comprehensive solution YOU ARE HERE
  • 20. 20© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential BEYOND DEEP LEARNING ENSEMBLE LEARNING Behavioral Analytics Internal Resource Access Finance servers Authentication AD logins Remote Access VPN logins External Activity C&C, personal email SaaS Activity Office 365, Box Cloud IaaS AWS, Azure Physical Access badge logs Exfiltration DLP, Email Ensemble approach using a mix of different models over various types of behaviors from the same entity
  • 21. 21© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential BEYOND DEEP LEARNING REINFORCEMENT LEARNING Models Alerts User Feedback Interactive Learning Local Context Input Data Self Learning Initial Parameters
  • 22. 22© 2016 Niara Inc. All Rights reserved. Proprietary and Confidential USER & ENTITY BEHAVIOR ANALYTICS UEBA SECURITY what is UEBA UEBA SOLUTION infrastructure needed to deep learning BEYOND DEEP LEARNING how to build a comprehensive solution