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[object Object],[object Object],[object Object],[object Object],ALADIN: Active Learning for Statistical Intrusion Detection NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security 12/8/2007
Motivation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security 12/8/2007
Active Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security 12/8/2007
ALADIN ,[object Object],[object Object],[object Object],12/8/2007 NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security
Choosing Samples for Labeling – Active Anomaly Detection ,[object Object],[object Object],[object Object],[object Object],[object Object],12/8/2007 NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security
Choosing Samples for Labeling – Active Learning ,[object Object],[object Object],[object Object],[object Object],12/8/2007 NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security
ALADIN: Combines Active Anomaly Detection and Active Learning NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security 12/8/2007
Classification Stage ,[object Object],[object Object],[object Object],[object Object],[object Object],12/8/2007 NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security
Modeling Stage ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],12/8/2007 NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security
Network Intrusion Detection Results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security 12/8/2007
Results – Anomaly Detection 12/8/2007 NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security
Results – Prediction Accuracy 12/8/2007 NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security
FP/FN Per Class NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security 12/8/2007 True Label Num Labeled Samples True Predicted Label TP Count Incorrectly Predicted Label FN Count FP Rate FN Rate normal 551 normal 55715 satan 3 4.12% 0.20% guess_passwd 10 ipsweep 67 back 2 neptune 57 neptune 20425 0.00% 0.00% smurf 82 smurf 18904 normal 7 0.00% 0.04% back 36 back 5 normal 1961 0.00% 99.75% ipsweep 58 ipsweep 675 normal 27 0.07% 3.85% satan 49 satan 470 normal 20 0.00% 4.08% portsweep 54 portsweep 223 normal 1 0.00% 0.45%
Malware Detection on Microsoft Network Logs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security 12/8/2007
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security 12/8/2007
[object Object],[object Object],[object Object],[object Object],ALADIN: Active Learning for Statistical Intrusion Detection NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security 12/8/2007

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slides

  • 1.
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7. ALADIN: Combines Active Anomaly Detection and Active Learning NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security 12/8/2007
  • 8.
  • 9.
  • 10.
  • 11. Results – Anomaly Detection 12/8/2007 NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security
  • 12. Results – Prediction Accuracy 12/8/2007 NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security
  • 13. FP/FN Per Class NIPS Workshop 2007 – Machine Learning in Adversarial Environments for Computer Security 12/8/2007 True Label Num Labeled Samples True Predicted Label TP Count Incorrectly Predicted Label FN Count FP Rate FN Rate normal 551 normal 55715 satan 3 4.12% 0.20% guess_passwd 10 ipsweep 67 back 2 neptune 57 neptune 20425 0.00% 0.00% smurf 82 smurf 18904 normal 7 0.00% 0.04% back 36 back 5 normal 1961 0.00% 99.75% ipsweep 58 ipsweep 675 normal 27 0.07% 3.85% satan 49 satan 470 normal 20 0.00% 4.08% portsweep 54 portsweep 223 normal 1 0.00% 0.45%
  • 14.
  • 15.
  • 16.