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Advances in cloud scale machine learning for cyber-defense

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Priyanka Aash
Priyanka Aash

Picking an attacker’s signals out of billions of log events in near real time from petabyte scale storage is a daunting task, but Microsoft has been using security data science at cloud scale to successfully disrupt attackers. This session will present the latest frameworks, techniques and the unconventional machine-learning algorithms that Microsoft uses to protect its infrastructure and customers. (Source : RSA Conference USA 2017)

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SESSION ID:SESSION ID: #RSAC Mark Russinovich Advances in Cloud-Scale Machine Learning for Cyber-Defense EXP – T11 CTO, Microsoft Azure Microsoft Corporation @markrussinovich
#RSAC SQL Server + R Microsoft R Server Hadoop + R Spark + R Microsoft CNTK Azure Machine Learning Cortana Intelligence Suite R Tools/Python Tools for Visual Studio Azure Notebooks (JuPyTer) Cognitive Services Bot Framework Cortana Office 365 Bing Skype Xbox 360 Dynamics 365HoloLens Intelligence in every software
#RSAC Microsoft’s daily cloud security scale 10s of PBs of logs 300+ million active Microsoft Account users Detected/ reflected attacks >10,000 location-detected attacks 1.5 million compromise attempts deflected 1.3+ billion Azure Active Directory logons
#RSAC WHAT IS ATTACK DISRUPTION?
#RSAC Red Team Kill Chain Recon Delivery Persist Move ElevateFoothold Exfiltrate
#RSAC Blue Team Kill Chain Recon Delivery Persist Move ElevateFoothold Exfiltrate Gather Detect Triage Context PlanAlert Execute for Attack Detection
#RSAC Blue Team Kill Chain Recon Delivery Persist Move ElevateFoothold Exfiltrate Gather Detect Triage Context PlanAlert Execute for Attack Disruption
#RSAC False Positives Manual Triage Challenges for Attack Disruption
#RSAC False Positives Lose ability to triage Recon Delivery Persist Move ElevateFoothold Exfiltrate Gather Detect Triage Context PlanAlert Execute
#RSAC False Positives FACT You cannot salvage a false positive with just Visualization. You need better solutions.
#RSAC False Positives Evolution of security detection techniques Data Program/Rules Output TRADITIONAL PROGRAMMING MACHINE LEARNING Hand-crafted rules by security professionals Con: Rules are static, and don’t change with changes in environment => False Positives! Data Output/Labels Program System adapts to changes in environment as new data is provided, and re-trained Our supervised learning approach enables detection without generating many FPs
#RSAC Labels in Microsoft For supervised learning, Azure gets labeled data through: Domain experts, customers who provide feedback from Alerts Automated Attack bots Labels from other product groups (including O365, Windows Seville) Surgical Red team exercises (OneHunt) Bug Bounty MSRC
#RSAC False Positives Manual Triage
#RSAC Manual Triage Gather Detect Triage Context PlanAlert Execute For Attack Disruption, we need to think beyond detection We need to change this
#RSAC Adaptable Successful Detection Explainable Actionable Properties of a Successful Machine Learning Solution
#RSAC EVOLVING ATTACKS Constantly changing environments leads to constantly changing attacks • New services • New features for existing services EVOLVING LANDSCAPE Frequent deployments New services coming online Usage spikes Adaptable Adaptable in Cloud is Difficult Why?
#RSAC Adaptable Explainability Why? Surfacing a security event to an end-user can be useless if there is no explanation Explainability of results should be considered at earliest possible stage of development Explainable Best detection signal with no explanation might be dismissed/overlooked <Example – How do you explain this to an analyst> UserId Time EventId Feature1 Feature2 Feature3 Feature4 … Score 1a4b43 2016-09-01 02:01 4688 0.3 0.12 3.9 20 … 0.2 73d87a 2016-09-01 03:15 4985 0.4 0.8 0 11 … 0.09 9ca231 2016-09-01 05:10 4624 0.8 0.34 9.2 7 … 0.9 5e9123 2016-09-01 05:32 4489 2.5 0.85 7.6 2.1 … 0.7 1e6a7b 2016-09-01 09:12 4688 3.1 0.83 3.6 6.2 … 0.1 33d693 2016-09-01 14:43 4688 4.1 0.63 4.7 5.1 … 0.019 7152f3 2016-09-01 19:11 4688 2.7 0.46 3.9 1.4 … 0.03 Results without explanation are hard to interpret
#RSAC Adaptable Actionable Detections Detections must result in downstream action Good explanation without being actionable is of little value Explainable • Policy decisions • Reset user password EXAMPLES Actionable
#RSAC Adaptable Explainable Actionable Basic Advanced Sophistication of Algorithms Less UsefulMore Useful Usefulness of Alerts Successful Detection Outlier Successful Detections incorporate domain knowledge through disparate datasets and rules Security Interesting Alerts Framework for a Successful Detection Anomaly Security Domain Knowledge More Domain knowledgeLittle Domain knowledge
#RSAC Case Study 1 Successful detection through combining disparate datasets PROBLEM STATEMENT Detect compromised VMs in Azure HYPOTHESIS If the VM is sending spam, then it is most likely compromised. SOLUTION Use supervised Machine Learning to leverage Labeled spam data from Office365 and combine with IPFIX data from Azure.
#RSAC Case Study 1 Technique Overview IPFIX Features Azure SPAM Spam Tags come from O365! EXAMPLES Automated • All ports with traffic • Number of connections • Which TCP flags combination exist • Many more…
#RSAC IPFIX data Spam labeled IPFIX data Benign IPFIX data Case Study 1 Technique Overview New Case Automated Compromise Detection Machine Learning ü ü
#RSAC Dataset External IPs External Ports TCP flags Existence (binary) Counts Normalized counts FEATURE SOURCES FEATURE TYPES Case Study 1 WHY IS NETWORK DATA GOOD FOR DETECTION? ü No installation required – running on all Azure tenants ü No overload on the VM ü Resilient – cannot be maliciously turned off ü OS independent
#RSAC Results Case Study 1 Machine Learning Deep Dive: Gradient Boosting Input data for 1st iteration Weak learner at 1st iteration
#RSAC Results Case Study 1 Machine Learning Deep Dive: Gradient Boosting Input data for 2nd iteration The data points that were incorrectly categorized by the weak learner in the first iteration (the positive examples) are now weighted more. Simultaneously, the correct points are down weighted. Learner at 2nd iteration
#RSAC Results Case Study 1 Machine Learning Deep Dive: Gradient Boosting Input data for 3rd iteration The data points that were incorrectly categorized in the second iteration (the negative examples) are now weighted more. Simultaneously, the correct points are down weighted. Learner at 3rd iteration + + Final result is a combination of learners from each iteration
#RSAC Model Performance and Productization Model trained in regular intervals Size of data: 360GB per day Within minutes Classification runs multiple times a day Completed within seconds Dataset True Positive Rate False Positive Rate Only using Azure IPFIX data 55% 1% Using Azure IPFIX and O365 data 81% 1% 26 points improvement Case Study 1
#RSAC Case Study 2 Successful detection through combining rules and machine learning PROBLEM STATEMENT Rule based malware detection place hard constraints if something is a malware or not. While they are specific, they have a lot of False Positives, False negatives and are not adaptable HYPOTHESIS Can we combine the hard logic of rule based detections with the soft - logic of machine learning systems? SOLUTION Build two ML models: 1) Model 1 that baselines malware behavior 2) Model 2 that incorporates rules as features Combine result of two models
#RSAC Conventional A/V Detonation Chamber Spin up multiple VMs Multiple OS and Office versions Instrument attachment behavior Safelinks Protects against malicious URLs in Real Time (on click) Case Study 2 MALWARE DETECTION BACKGROUND ATP Architecture
#RSAC Combined Verdict Combiner Fingerprint Model Behavioral Model Case Study 2 • Hash/Fuzzy Hash • PE Analyzer • File Type Analyzer • PhotoSimilarity • … • SysMon • ETW Logger • API hooks • Crash dump • … • YARA • Threat Intel • Network Analysis • Macro Evidence • … PRE-ANALYSIS DETONATION POST-ANALYSIS Technique Overview
#RSAC Case Study 2 Dataset (SAMPLE)
#RSAC Case Study 2 Machine Learning Deep Dive: Fingerprint Model Information gets more granular Call Order Level 1 Level 2 Level 3 Level 4 Level 5 1 Process LoadImage SYSTEM .exe wscript 2 Api CallFunction CreateMutexA _!MSFTHISTORY!_ 3 Api CallFunction CreateMutexW !IETld!Mutex 4 Registry SetRegValue Tracing wscript_rasapi32 EnableTracing 5 Registry DeleteRegValue InternetOption internet settings ProxyBypass 6 Process CreateProcess NOT_SANDBOX_CHECK LaunchedViaCom 7 Network AccessNetWork Wininet_Getaddrinfo 8 Api CallFunction CreateMutexW RANDOM_STR 9 Network ResolveHost piglyeleutqq.com UNKNOWN 10 Api CallFunction Connect UNKNOWN
#RSAC Case Study 2 Machine Learning Deep Dive: Fingerprint Model Observations Benefits of the Action-Chain prototype • It can be RESILIENT to malware obfuscation because it captures the runtime semantics by considering the more IMPORTANT details • Feature extraction is NON-PARAMETRIC • Would generalize to many situations Model Current: L1 Logistic Regression followed by L2 Logistic Regression; weighted samples through cross-validation
#RSAC Case Study 2 Machine Learning Deep Dive: Behavioral Model Incorporates security domain knowledge into the model Source of features • YARA rules • Static analysis • Aggregates from Data: • Registry keys/values that are changed/created/deleted • Mutexes created • Number of spawn processes per process detail info The model works well to detect new types malware
#RSAC Model Performance and Productization Model trained in regular intervals Size of data: 270GB per day Completed within minutes Classification runs multiple times a day Completed within milliseconds 10 points improvement Dataset True Positive Rate False Positive Rate YARA rules only 82.6% 0.0178% Machine Learning Model 1 + Model 2 93.6% 0.0127% Case Study 2
#RSAC Gather Detect Triage Context PlanAlert Execute For Attack Disruption, We Need to Think Beyond Detection We need to change this
#RSAC incidents, not alerts Anomalous DLL: rundll32.exe launched as sposql11 on CFE110095 New process uploading: rundll32.exe to 40.114.40.133 on CFE110095 Large transfer: 50MB to 40.114.40.133 from sqlagent.exe on SQL11006 Triage anomalousdll sposql11 CFE110095rundll32.exe newprocupload 40.114.40.133sqlagent.exe largetransfer SQL11006 alert type process user host alert type process remote host host alert type remote host process host
#RSAC incidents, not alertsTriage
#RSAC Demo Demo
#RSAC Attack Disruption means to shorten blue team kill chain Speed Real-time detection Conclusion Quality Reduce false positives React Fast triage
#RSAC Attack Disruption Checklist Azure Event Hubs Combine different datasets Labels, Labels, Labels Scalable ML solution and expertise Example Azure services you can leverage: Azure Machine Learning Azure Data Lake
#RSAC Thank you

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