Scaling ML-Based Threat Detection For Production Cyber Attacks

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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.

Données & analyses

WIFI SSID:SparkAISummit | Password: UnifiedAnalytics

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
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Director of Data Science,
GSI Technology

Scary Trends
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There is a hacker attack now every 39 seconds
Denial-of-service attacks up 140%
Cost of breach at $150 million

Opportunities
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Cybersecurity spending tops $100 billion
Funding of cybersecurity startups up 25%
Shortfall of 3 million cybersecurity workers

Opportunities
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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

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ML + Cybersecurity + Production

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ML + Cybersecurity

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ML + Cybersecurity

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ML + Cybersecurity
X

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ML + Cybersecurity
X
When ML and Data Science are the death of a
good company: A cautionary tale.X

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ML + Cybersecurity
● AI “Washing”
● Model Transparency
● Model Bias
● Adversarial Attacks

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Adversarial Attack
Image From OpenAI

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ML + Cybersecurity
ML is just another tool
and not a silver bullet
for cybersecurity!

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Cybersecurity + Production

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Cybersecurity + Production

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ML + Production Systems

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ML + Production Systems
Image from
Brendan Greg

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ML + Production Systems
Image from
Brendan Greg

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Putting It All Together...

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Putting It All Together...

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Why?
Custom Chip PCIe Boards Super Cluster

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Chip SuperCluster Cloud
● Lots and lots of custom chips
● Linux OS
● Managed Hosting
○ Drug Discovery
○ Government
○ Aerospace

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Defense In Depth
NETWORK
FILE SYSTEM
HOST

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Defense In Depth
Perimeter Protection
Malware/AV Detection
Host Is Still Vulnerable
○ Credential Theft, Session Hijacking
○ Insider Threats
○ Active Monitoring !

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Anomaly Detection
● Cache Side Channel
○ Spectre/Meltdown
● Control Flow Attacks
○ ROP
● Anomalous User Behavior
○ Exfiltration

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Anomaly Detection
● Cache Side Channel
○ Spectre/Meltdown ← CPU Performance Counters
● Control Flow Attacks
○ ROP ← Call Stack Sampling
● Anomalous User Behavior
○ Exfiltration ← File System Monitoring

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Supervised and Unsupervised
X
2
X
2
X
1
X
1
Boundary
Clusters

Data Pipeline
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Spark ClusterData Source

Raw Data Source
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perf
○ Hardware stats
eBPF
○ Latest linux kernels
Python interface
○ BCC library

Perf, eBPF Kafka
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Kafka Ingestion
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Real-Time Data
○ For ML Inference
Historical Data
○ For ML Training
○ For Baselining (AD)

Training and Inference
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Baseline
○ For AD
ML Training
○ SVM
○ Regression
○ Auto-Encoder

Cache Side Channel Model
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Scenario: Cache Side Channel
Attack Detector
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Scenario: Cache Side Channel
Attack Detector
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normal
POC
production

Scenario: Cache Side Channel
Attack Detector
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normal
POC
production
TRAINING DATA
INFERENCE DATA

Code: Baseline Data
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Code: Training
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Code: Inference
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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
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We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark. Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue · Why? o Custom queries on top a table; We load the data once and query N times · Why not Structured Streaming · Working Solution using Redis Niche 2 : Distributed Counters · Problems with Spark Accumulators · Utilize Redis Hashes as distributed counters · Precautions for retries and speculative execution · Pipelining to improve performance

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Databricks

In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data. In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.

Re-imagine Data Monitoring with whylogs and Spark

Databricks

Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components. We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure. This allows us to introduce optimization rules that (i) reduce unnecessary computations by passing information between the data processing and ML operators (ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and (iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator. We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.

Raven: End-to-end Optimization of ML Prediction Queries

Databricks

Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis. Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them. Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy. This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.

Processing Large Datasets for ADAS Applications using Apache Spark

Databricks

At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences. What are we storing? Multi Source – Multi Channel Problem Data Representation and Nested Schema Evolution Performance Trade Offs with Various formats Go over anti-patterns used (String FTW) Data Manipulation using UDFs Writer Worries and How to Wipe them Away Staging Tables FTW Datalake Replication Lag Tracking Performance Time!

Massive Data Processing in Adobe Using Delta Lake

Databricks

Plus de Databricks (20)

DW Migration Webinar-March 2022.pptx

Data Lakehouse Symposium | Day 1 | Part 1

Data Lakehouse Symposium | Day 1 | Part 2

Data Lakehouse Symposium | Day 2

Data Lakehouse Symposium | Day 4

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

Democratizing Data Quality Through a Centralized Platform

Learn to Use Databricks for Data Science

Why APM Is Not the Same As ML Monitoring

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Stage Level Scheduling Improving Big Data and AI Integration

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Scaling your Data Pipelines with Apache Spark on Kubernetes

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Sawtooth Windows for Feature Aggregations

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Re-imagine Data Monitoring with whylogs and Spark

Raven: End-to-end Optimization of ML Prediction Queries

Processing Large Datasets for ADAS Applications using Apache Spark

Massive Data Processing in Adobe Using Delta Lake

Dernier

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This project demonstrates a machine learning approach to detecting credit card fraud using advanced algorithms and techniques. The project utilizes a dataset containing various features such as transaction amount, merchant location, time of transaction, and others to build a predictive model. The presentation covers data preprocessing steps, feature engineering techniques, and the selection of machine learning algorithms such as logistic regression or random forest. It also discusses model evaluation metrics and the importance of fraud detection in financial institutions for safeguarding against fraudulent activities. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Detecting Credit Card Fraud: A Machine Learning Approach

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This project aims to predict whether a loan application will be approved or denied based on various factors such as applicant's income, credit score, loan amount, etc. Using a dataset containing historical loan application data, we employed machine learning algorithms to build a predictive model. The model was trained on features such as applicant's income, credit history, loan amount, loan term, and others. After training the model, we evaluated its performance using metrics like accuracy, precision, recall, and F1 score. The insights from this project can help financial institutions streamline their loan approval process and make informed decisions. Visit for more information: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Predicting Loan Approval: A Data Science Project

Boston Institute of Analytics

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1. WIFI SSID:SparkAISummit | Password: UnifiedAnalytics

2. George Williams, GSI Technology Scaling ML-based CyberThreat Detection For Production Systems #UnifiedAnalytics #SparkAISummit

3. Agenda ● Cybersecurity Trends ● ML + Cybersecurity + Production Systems ● GSI Technology ● Architecture ● Code 3#UnifiedAnalytics #SparkAISummit Director of Data Science, GSI Technology

4. 4#UnifiedAnalytics #SparkAISummit

5. 5#UnifiedAnalytics #SparkAISummit

6. 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

7. Opportunities 7#UnifiedAnalytics #SparkAISummit Cybersecurity spending tops $100 billion Funding of cybersecurity startups up 25% Shortfall of 3 million cybersecurity workers

8. 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. 9#UnifiedAnalytics #SparkAISummit ML + Cybersecurity + Production

10. 10#UnifiedAnalytics #SparkAISummit ML + Cybersecurity

11. 11#UnifiedAnalytics #SparkAISummit ML + Cybersecurity

12. 12#UnifiedAnalytics #SparkAISummit ML + Cybersecurity X

13. 13#UnifiedAnalytics #SparkAISummit ML + Cybersecurity X When ML and Data Science are the death of a good company: A cautionary tale.X

14. 14#UnifiedAnalytics #SparkAISummit ML + Cybersecurity ● AI “Washing” ● Model Transparency ● Model Bias ● Adversarial Attacks

15. 15#UnifiedAnalytics #SparkAISummit Adversarial Attack Image From OpenAI

16. 16#UnifiedAnalytics #SparkAISummit ML + Cybersecurity ML is just another tool and not a silver bullet for cybersecurity!

17. 17#UnifiedAnalytics #SparkAISummit Cybersecurity + Production

18. 18#UnifiedAnalytics #SparkAISummit Cybersecurity + Production

19. 19#UnifiedAnalytics #SparkAISummit ML + Production Systems

20. 20#UnifiedAnalytics #SparkAISummit ML + Production Systems Image from Brendan Greg

21. 21#UnifiedAnalytics #SparkAISummit ML + Production Systems Image from Brendan Greg

22. 22#UnifiedAnalytics #SparkAISummit Putting It All Together...

23. 23#UnifiedAnalytics #SparkAISummit Putting It All Together...

24. 24#UnifiedAnalytics #SparkAISummit Why? Custom Chip PCIe Boards Super Cluster

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30. 30#UnifiedAnalytics #SparkAISummit Supervised and Unsupervised X 2 X 2 X 1 X 1 Boundary Clusters

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35. Training and Inference 35#UnifiedAnalytics #SparkAISummit Baseline ○ For AD ML Training ○ SVM ○ Regression ○ Auto-Encoder

36. Cache Side Channel Model 36#UnifiedAnalytics #SparkAISummit

37. Scenario: Cache Side Channel Attack Detector 37#UnifiedAnalytics #SparkAISummit

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39. Scenario: Cache Side Channel Attack Detector 39#UnifiedAnalytics #SparkAISummit normal POC production TRAINING DATA INFERENCE DATA

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41. Code: Training 41#UnifiedAnalytics #SparkAISummit

42. Code: Inference 42#UnifiedAnalytics #SparkAISummit

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44. Schedule / Contact Chip Cluster: Q4 2019 Github: HoneycombSecurity Project Medium: GSI Technology Blog Twitter: @cgeorgewilliams 44#UnifiedAnalytics #SparkAISummit

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Scaling ML-Based Threat Detection For Production Cyber Attacks

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