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Scaling ML-Based Threat Detection For Production Cyber Attacks

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Databricks
Databricks

Vulnerabilities such as Spectre and Meltdown continue to plague many production servers, based on Intel CPUs. Our solution involves software-based monitoring of hardware counters and sending that data to Apache Spark clusters for threat detection. We leverage Spark's support for support vector machine (SVM) inference. Our machine learning models are trained off-line by a data scientist within a Jupyter notebook environment. As new models are validated, they can be easily deployed to the Spark cluster from the notebook. We have standardized model export and import using the ONNX machine learning open file format. In our presentation, we will demo the full pipeline, from model training to deployment. We will discuss the various challenges when deploying ML-based cyber-threat detection at scale using Apache Spark. For example, we found that gaps in detection can occur when Spark models are updated. We will describe a novel data ingestion architecture, based on Apache Kafka, that we developed to deal with this issue.

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George Williams, GSI Technology Scaling ML-based CyberThreat Detection For Production Systems #UnifiedAnalytics #SparkAISummit
Agenda ● Cybersecurity Trends ● ML + Cybersecurity + Production Systems ● GSI Technology ● Architecture ● Code 3#UnifiedAnalytics #SparkAISummit Director of Data Science, GSI Technology
4#UnifiedAnalytics #SparkAISummit
5#UnifiedAnalytics #SparkAISummit
Scary Trends 6#UnifiedAnalytics #SparkAISummit There is a hacker attack now every 39 seconds Denial-of-service attacks up 140% Cost of breach at $150 million
Opportunities 7#UnifiedAnalytics #SparkAISummit Cybersecurity spending tops $100 billion Funding of cybersecurity startups up 25% Shortfall of 3 million cybersecurity workers
Opportunities 8#UnifiedAnalytics #SparkAISummit Cybersecurity spending tops $100 billion Funding of cybersecurity startups up 25% Shortfall of 3 million cybersecurity workers Hottest tech jobs: Security DevOps Eng & Data Eng/Sci
9#UnifiedAnalytics #SparkAISummit ML + Cybersecurity + Production
10#UnifiedAnalytics #SparkAISummit ML + Cybersecurity
11#UnifiedAnalytics #SparkAISummit ML + Cybersecurity
12#UnifiedAnalytics #SparkAISummit ML + Cybersecurity X
13#UnifiedAnalytics #SparkAISummit ML + Cybersecurity X When ML and Data Science are the death of a good company: A cautionary tale.X
14#UnifiedAnalytics #SparkAISummit ML + Cybersecurity ● AI “Washing” ● Model Transparency ● Model Bias ● Adversarial Attacks
15#UnifiedAnalytics #SparkAISummit Adversarial Attack Image From OpenAI
16#UnifiedAnalytics #SparkAISummit ML + Cybersecurity ML is just another tool and not a silver bullet for cybersecurity!
17#UnifiedAnalytics #SparkAISummit Cybersecurity + Production
18#UnifiedAnalytics #SparkAISummit Cybersecurity + Production
19#UnifiedAnalytics #SparkAISummit ML + Production Systems
20#UnifiedAnalytics #SparkAISummit ML + Production Systems Image from Brendan Greg
21#UnifiedAnalytics #SparkAISummit ML + Production Systems Image from Brendan Greg
22#UnifiedAnalytics #SparkAISummit Putting It All Together...
23#UnifiedAnalytics #SparkAISummit Putting It All Together...
24#UnifiedAnalytics #SparkAISummit Why? Custom Chip PCIe Boards Super Cluster
25#UnifiedAnalytics #SparkAISummit Chip SuperCluster Cloud ● Lots and lots of custom chips ● Linux OS ● Managed Hosting ○ Drug Discovery ○ Government ○ Aerospace
26#UnifiedAnalytics #SparkAISummit Defense In Depth NETWORK FILE SYSTEM HOST
27#UnifiedAnalytics #SparkAISummit Defense In Depth Perimeter Protection Malware/AV Detection Host Is Still Vulnerable ○ Credential Theft, Session Hijacking ○ Insider Threats ○ Active Monitoring !
28#UnifiedAnalytics #SparkAISummit Anomaly Detection ● Cache Side Channel ○ Spectre/Meltdown ● Control Flow Attacks ○ ROP ● Anomalous User Behavior ○ Exfiltration
29#UnifiedAnalytics #SparkAISummit Anomaly Detection ● Cache Side Channel ○ Spectre/Meltdown ← CPU Performance Counters ● Control Flow Attacks ○ ROP ← Call Stack Sampling ● Anomalous User Behavior ○ Exfiltration ← File System Monitoring
30#UnifiedAnalytics #SparkAISummit Supervised and Unsupervised X 2 X 2 X 1 X 1 Boundary Clusters
Data Pipeline 31#UnifiedAnalytics #SparkAISummit Spark ClusterData Source
Raw Data Source 32#UnifiedAnalytics #SparkAISummit perf ○ Hardware stats eBPF ○ Latest linux kernels Python interface ○ BCC library
Perf, eBPF Kafka 33#UnifiedAnalytics #SparkAISummit
Kafka Ingestion 34#UnifiedAnalytics #SparkAISummit Real-Time Data ○ For ML Inference Historical Data ○ For ML Training ○ For Baselining (AD)
Training and Inference 35#UnifiedAnalytics #SparkAISummit Baseline ○ For AD ML Training ○ SVM ○ Regression ○ Auto-Encoder
Cache Side Channel Model 36#UnifiedAnalytics #SparkAISummit
Scenario: Cache Side Channel Attack Detector 37#UnifiedAnalytics #SparkAISummit
Scenario: Cache Side Channel Attack Detector 38#UnifiedAnalytics #SparkAISummit normal POC production
Scenario: Cache Side Channel Attack Detector 39#UnifiedAnalytics #SparkAISummit normal POC production TRAINING DATA INFERENCE DATA
Code: Baseline Data 40#UnifiedAnalytics #SparkAISummit
Code: Training 41#UnifiedAnalytics #SparkAISummit
Code: Inference 42#UnifiedAnalytics #SparkAISummit
43#UnifiedAnalytics #SparkAISummit More... ● Model Validation (FP,FN), Hyperparameters ?? ● Automated and Continuous Learning ● Adversarial Attacks ● Persistence: Models (ONNX), Forensics (Cass) ● Structured Streaming, Pipelines ● Networking/Local Inference
Schedule / Contact Chip Cluster: Q4 2019 Github: HoneycombSecurity Project Medium: GSI Technology Blog Twitter: @cgeorgewilliams 44#UnifiedAnalytics #SparkAISummit
DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT The picture can't be displayed.

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