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Extending Splunk with Machine-learning Predictive Analytics

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Splunk
Splunk

The document discusses using machine learning for anomaly detection in Splunk. It describes how machine learning can automatically learn baseline behavior, overcome limitations of human analysis, and detect anomalous behavior. Specifically, it explains how machine learning builds probability distribution functions from historical data to determine what events are unexpected or outliers. This allows identifying anomalies without needing predefined thresholds.

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Extending Splunk with Machine-learning Predictive Analytics Rich Collier Solutions Architect rich@prelert.com
Why Machine Learning? • Overcome limitations of human analysis • Auto-learn baseline behavior using proper modeling • Detect anomalous behavior
Why Machine Learning? • Overcome limitations of human analysis • Auto-learn baseline behavior using proper modeling • Detect anomalous behavior
Overcoming limitations of Human Analysis • Judging what’s “normal” is not always easy • Humans don’t always choose the right techniques
IPTables (firewall) • How to find most anomalous users (aggressive brute force attackers)? • Here is a typical (manual) process
Step 1) Search Questions: What’s normal? What about that spike? Probably should try to visualize counts by SRC over time…
Step 2) stats command, sort by count Question: How to show as a function of time, not just overall?
Step 3) add bucketing for breakdown by time Question: What is an anomalous count per bucket? 100? 1000? 10,000? Maybe we should try to use some more stats?
Step 4) add some “basic” statistical analysis: avg +/- 2 Question: How to show the individual “outliers” (and not lose the concept of time)?
Step 5) use eventstats to repair time problem and add “where” clause to only show those outside of +/-2 Question: Are these 161 results accurate? (I hope you didn’t build an alert and get 161 of them!)
Problem: Statistical modeling is INCORRECT for this data – (-75) events doesn’t make sense for avg - 2 – how much confidence do you have in avg + 2 ? Result: • Wrong model= false positives/negatives
The Problem: +/-2 assumes data is Gaussian (Bell Curve) Clearly, this data is better fit by a Poisson curve
Examples of Non-Gaussian Data status=503 Memory Utilization CPU load status=404 Revenue Transactions
One More Problem… • Even if the demonstrated technique was accurate: – Still need to persist what you’ve learned “so far” so that you don’t have to keep re-inspecting historical data as new data comes in – This requires you to manually write/read information into a summary index
Why Machine Learning? • Overcome limitations of human analysis • Auto-learn baseline behavior using proper modeling • Detect anomalous behavior
First, an Analogy • How could I accurately predict how much Postal-mail you are likely to get delivered to your home tomorrow?
I Would… • Watch your mail delivery for a while – 1 day? – 1 week? – 1 month? – 1 year? • Use my observations to create a…
Average? Std. Deviation? Probability Distribution Function?
A Probability Distribution Function! % likelihood (probability) Best for my house pieces of mail per day
A Probability Distribution Function! % likelihood (probability) College Student? pieces of mail per day
% likelihood (probability) A Probability Distribution Function! My Mom pieces of mail per day
Using Machine Learning to build a Probability Distribution Function • PDF must be built specifically for each “instance” • PDF should be constructed automatically merely by watching the data
Using Machine Learning to build a Probability Distribution Function 23
Now what?
Why Machine Learning? • Overcome limitations of human analysis • Auto-learn baseline behavior using proper modeling • Detect anomalous behavior
Finding “what’s unexpected”… Your job is often looking for unexpected change in your environment, either proactively through monitoring or reactively through diagnostics/troubleshooting
% likelihood (probability) Using the PDF to Find What is Unexpected zero pieces of mail? fifteen pieces of mail? pieces of mail per day
Relate back to data in Splunk • # Pieces of mail = # events of a certain type – number of failed logins – number of errors of different types – number of events with certain status codes – etc. • Or, performance metrics – response time – utilization %
Back to our Example!
• Prelert Anomaly Detective – Automatically, and correctly models data via self-learning – Applies sophisticated Bayesian techniques – Persists “on-going” analysis to allow real-time alerting – Makes it easy to use 3 significant alerts, not 161!
• Results are: – Accurate outliers – Automatically clustered and scored by their probabilistic “unlikelihood” – Relevant in time, easy to make alerts – Clickable for drill-down
• Drill-downs: – Automatically constructs useful search syntax and time selection – Shows anomalies in context of the original data – Serve as a possible jumping-off point for subsequent manual mining
Automated Anomaly Detection • Less time searching & troubleshooting • Proactive trustworthy alerts without thresholds • Auto-discovers the previously unknown
Automated Anomaly Detection for splunk> Additional Use Cases
Use Case • Data sources: – App logs – Network performance – SQL-Server metrics • Prelert identifies network discards that cause app to disconnect from DB Correlating Anomalies Across Data Types
Use Case • Data source: Netstat • Prelert finds a rare FTP connection from a server that doesn’t normally use FTP Servers making unusual TCP connections
Use Case • Data source: Custom logs • Prelert identifies unusual $0.60 transaction – traced to bug in currency conversion Revenue Transactions
Use Case • Data source: BlueCoat proxy • Prelert identifies users abusing Internet privileges gambling sites porn sites Clients pervasively visiting rare URLs
Use Case • Response time of online bank website • Prelert alerts on spikes without the need to create a single threshold Monitoring Performance w/o Thresholds
Use Case • Data source: BlueCoat proxy • Prelert identifies client attempting to exploit an outside IIS webserver Unusual outbound traffic rates
Automated Anomaly Detection for splunk>

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Extending Splunk with Machine-learning Predictive Analytics

  • 1. Extending Splunk with Machine-learning Predictive Analytics Rich Collier Solutions Architect rich@prelert.com
  • 2. Why Machine Learning? • Overcome limitations of human analysis • Auto-learn baseline behavior using proper modeling • Detect anomalous behavior
  • 3. Why Machine Learning? • Overcome limitations of human analysis • Auto-learn baseline behavior using proper modeling • Detect anomalous behavior
  • 4. Overcoming limitations of Human Analysis • Judging what’s “normal” is not always easy • Humans don’t always choose the right techniques
  • 5. IPTables (firewall) • How to find most anomalous users (aggressive brute force attackers)? • Here is a typical (manual) process
  • 6. Step 1) Search Questions: What’s normal? What about that spike? Probably should try to visualize counts by SRC over time…
  • 7. Step 2) stats command, sort by count Question: How to show as a function of time, not just overall?
  • 8. Step 3) add bucketing for breakdown by time Question: What is an anomalous count per bucket? 100? 1000? 10,000? Maybe we should try to use some more stats?
  • 9. Step 4) add some “basic” statistical analysis: avg +/- 2 Question: How to show the individual “outliers” (and not lose the concept of time)?
  • 10. Step 5) use eventstats to repair time problem and add “where” clause to only show those outside of +/-2 Question: Are these 161 results accurate? (I hope you didn’t build an alert and get 161 of them!)
  • 11. Problem: Statistical modeling is INCORRECT for this data – (-75) events doesn’t make sense for avg - 2 – how much confidence do you have in avg + 2 ? Result: • Wrong model= false positives/negatives
  • 12. The Problem: +/-2 assumes data is Gaussian (Bell Curve) Clearly, this data is better fit by a Poisson curve
  • 13. Examples of Non-Gaussian Data status=503 Memory Utilization CPU load status=404 Revenue Transactions
  • 14. One More Problem… • Even if the demonstrated technique was accurate: – Still need to persist what you’ve learned “so far” so that you don’t have to keep re-inspecting historical data as new data comes in – This requires you to manually write/read information into a summary index
  • 15. Why Machine Learning? • Overcome limitations of human analysis • Auto-learn baseline behavior using proper modeling • Detect anomalous behavior
  • 16. First, an Analogy • How could I accurately predict how much Postal-mail you are likely to get delivered to your home tomorrow?
  • 17. I Would… • Watch your mail delivery for a while – 1 day? – 1 week? – 1 month? – 1 year? • Use my observations to create a…
  • 19. A Probability Distribution Function! % likelihood (probability) Best for my house pieces of mail per day
  • 20. A Probability Distribution Function! % likelihood (probability) College Student? pieces of mail per day
  • 21. % likelihood (probability) A Probability Distribution Function! My Mom pieces of mail per day
  • 22. Using Machine Learning to build a Probability Distribution Function • PDF must be built specifically for each “instance” • PDF should be constructed automatically merely by watching the data
  • 23. Using Machine Learning to build a Probability Distribution Function 23
  • 25. Why Machine Learning? • Overcome limitations of human analysis • Auto-learn baseline behavior using proper modeling • Detect anomalous behavior
  • 26. Finding “what’s unexpected”… Your job is often looking for unexpected change in your environment, either proactively through monitoring or reactively through diagnostics/troubleshooting
  • 27. % likelihood (probability) Using the PDF to Find What is Unexpected zero pieces of mail? fifteen pieces of mail? pieces of mail per day
  • 28. Relate back to data in Splunk • # Pieces of mail = # events of a certain type – number of failed logins – number of errors of different types – number of events with certain status codes – etc. • Or, performance metrics – response time – utilization %
  • 29. Back to our Example!
  • 30. • Prelert Anomaly Detective – Automatically, and correctly models data via self-learning – Applies sophisticated Bayesian techniques – Persists “on-going” analysis to allow real-time alerting – Makes it easy to use 3 significant alerts, not 161!
  • 31. • Results are: – Accurate outliers – Automatically clustered and scored by their probabilistic “unlikelihood” – Relevant in time, easy to make alerts – Clickable for drill-down
  • 32. • Drill-downs: – Automatically constructs useful search syntax and time selection – Shows anomalies in context of the original data – Serve as a possible jumping-off point for subsequent manual mining
  • 33. Automated Anomaly Detection • Less time searching & troubleshooting • Proactive trustworthy alerts without thresholds • Auto-discovers the previously unknown
  • 34. Automated Anomaly Detection for splunk> Additional Use Cases
  • 35. Use Case • Data sources: – App logs – Network performance – SQL-Server metrics • Prelert identifies network discards that cause app to disconnect from DB Correlating Anomalies Across Data Types
  • 36. Use Case • Data source: Netstat • Prelert finds a rare FTP connection from a server that doesn’t normally use FTP Servers making unusual TCP connections
  • 37. Use Case • Data source: Custom logs • Prelert identifies unusual $0.60 transaction – traced to bug in currency conversion Revenue Transactions
  • 38. Use Case • Data source: BlueCoat proxy • Prelert identifies users abusing Internet privileges gambling sites porn sites Clients pervasively visiting rare URLs
  • 39. Use Case • Response time of online bank website • Prelert alerts on spikes without the need to create a single threshold Monitoring Performance w/o Thresholds
  • 40. Use Case • Data source: BlueCoat proxy • Prelert identifies client attempting to exploit an outside IIS webserver Unusual outbound traffic rates

Notas del editor

  1. [no audio here]
  2. Probability of data comes in all shapes and sizes – rarely does it fit a nice bell curve