John Cavazos Dept of Computer & Information Sciences University of Delaware

1. John Cavazos Dept of Computer & Information Sciences University of Delaware www.cis.udel.edu/~cavazos/cisc879 Applying Support Vector Machines for Intrusion Detection on Virtual Machines Lecture 6

2. Outline • Background and Motivation • Intrusion Detection Systems • Support Vector Machines (SVMs) • Dataset • Results • Conclusions Slides adapted from presentation by Fatemeh Azmandian (http://www.ece.neu.edu/~fazmandi)

3. Background • Virtual Machine: • A software implementation of a machine (computer) that executes programs like a real machine • Virtual Machine Monitor (VMM) or hypervisor: • The software layer providing the virtualization • Allows the multiplexing of the underlying physical machine between different virtual machines, each running its own operating system

4. Background (cont’d) • Intrusion Detection: • The process of monitoring the events occurring in a computer system or network and analyzing them for signs of intrusions • Intrusion: • An attempt to compromise: • Confidentiality • Integrity • Availability • An attempt to bypass the security mechanisms of a computer or network [1]

5. Background (cont’d) • Intrusion Detection System (IDS): • Software or hardware system that automates the process of monitoring the events occurring in a computer system or network, analyzing them for signs of security problems • Why is it important? • Every year, billions of dollars are lost due to virus attacks

6. Financial Impact of Virus Attacks

7. Intrusion Detection Approaches • Misuse Detection • Identifies intrusions based on known patterns for the malicious activity • Known patterns are referred to as signatures • Anomaly Detection • Identifies intrusions based on deviations from established normal behavior • Capable of identifying new (previously unseen) attacks • New normal behavior may be misclassified as abnormal, producing false positives

8. Intrusion Detection Systems • Host IDS (HIDS): • Performs intrusion detection from within host it is monitoring • Advantages: • Good visibility of the internal state of the host machine • Difficult for malicious code (malware) to evade the HIDS • Disadvantage: • Susceptible to attacks by malware

9. Intrusion Detection Systems • Network IDS (NIDS) • Performs intrusion detection through network connections and outside the host machine • Advantage: • More resistant to attacks by malware • Disadvantages: • Poor visibility of the internal state of the host machine • Easier for malware to evade the NIDS

10. Intrusion Detection Systems • VMM-based IDS: • Performs intrusion detection for a virtual machine through the Virtual Machine Monitor (VMM) • Advantages: • Better visibility of the internal state of the host machine, compared to an NIDS • Harder for malware to evade the IDS • Less susceptible to attacks by malware • Our goal is to create a VMM-based IDS using machine learning techniques • Support Vector Machines (SVMs)

11. VMM-IDS Overview

12. Support Vector Machines (SVMs) • Machine learning to classify data points into one of two classes • Two-Class SVMs • Training is done on data from two classes • One-Class SVMs • Training is done on data from only one class • During the testing phase, the origin and data points close to it are considered part of the second class

13. Slide Source: Andrew W. Moore Linear Classifiers a x f yest denotes +1 denotes -1 f(x,w,b) = sign(w. x- b) How would you classify this data?

14. Linear Classifiers a x f yest denotes +1 denotes -1 f(x,w,b) = sign(w. x- b) How would you classify this data? Slide Source: Andrew W. Moore

15. Linear Classifiers a x f yest denotes +1 denotes -1 f(x,w,b) = sign(w. x- b) How would you classify this data? Slide Source: Andrew W. Moore

16. Linear Classifiers a x f yest denotes +1 denotes -1 f(x,w,b) = sign(w. x- b) How would you classify this data? Slide Source: Andrew W. Moore

17. Linear Classifiers a x f yest denotes +1 denotes -1 f(x,w,b) = sign(w. x- b) How would you classify this data? Slide Source: Andrew W. Moore

18. Classifier Margin a x f yest denotes +1 denotes -1 f(x,w,b) = sign(w. x- b) Define the margin of a linear classifier as the width that the boundary could be increased by before hitting a datapoint Slide Source: Andrew W. Moore

19. Maximum Margin a x f yest denotes +1 denotes -1 f(x,w,b) = sign(w. x- b) The maximum margin linear classifieris the linear classifier with the maximum margin. This is the simplest kind of SVM (Called an LSVM) Linear SVM Slide Source: Andrew W. Moore

20. Maximum Margin a x f yest denotes +1 denotes -1 f(x,w,b) = sign(w. x- b) The maximum margin linear classifieris the linear classifier with the maximum margin. This is the simplest kind of SVM (Called an LSVM) Support Vectors are those datapoints that the margin pushes up against Linear SVM Slide Source: Andrew W. Moore

21. Suppose 1-dimension What would SVMs do with this data? x=0

22. Suppose 1-dimension Not a big surprise x=0 Positive “plane” Negative “plane”

23. Harder 1-dimensional dataset What can be done about this? x=0

24. Harder 1-dimensional dataset Use a kernel function to project the data onto higher dimensional space x=0

25. Harder 1-dimensional dataset Use a kernel function to project the data onto higher dimensional space x=0

26. Non-linear SVMs: Feature spaces Φ: x→φ(x) Input space Feature space

27. f(.) f( ) f( ) f( ) f( ) f( ) f( ) f( ) f( ) f( ) f( ) f( ) f( ) f( ) f( ) f( ) f( ) f( ) f( ) Feature space Input space Non-linear SVMs: Feature spaces • Kernel functions are used to transform data into a different, linearly separable feature space

28. Non-linear SVMs:Kernel Functions • Popular Kernel Functions: • Linear kernel • Polynomial Kernel • Gaussian Radial Basis Function (RBF) kernel • Sigmoid kernel

29. Dataset • Synthetic dataset based on SQL and AsteriskNow workload • Process-level features • Rate-based features • Correlation-based features • Time-based windows of execution • Current window size: 50 interrupt timers • Three normal datasets per workload • Two abnormal datasets per workload • Consists of both normal and abnormal data points

30. Constructing Features

31. Features

32. Two-Class SVM Results

33. SQL Train on Abn1 and Test on Abn2: Time Series Plot

34. SQL Train : Train on Abn1 and Test on Abn2: (ROC Curve)

35. SQL Train on Abn2 and Test on Abn1: Time Series Plot

36. SQL Train on Abn2 and Test on Abn1: ROC Curve

37. Conclusions • Two-class SVM can perform well in detecting intrusions in virtual machine environments • Goal to develop accurate intrusion detection system for VMs based on machine learning techniques

38. References [1] R. Bace and P. Mell. Intrusion Detection Systems. NIST Special PublicationsSP 800-31, November, 2001. [2] T. Garfinkel and M. Rosenblum. A Virtual Machine Introspection Based Architecture for Intrusion Detection. Proceedings of the Network and Distributed Systems Security Symposium, 2003. [3] Andrew Moore’s slides on Support Vector Machines http://www.cs.cmu.edu/~awm/tutorials [4] Prasad’s slides on Support Vector Machines www.cs.wright.edu/~tkprasad/courses/cs499/L18SVM.ppt [5] 2005 Malware Report: Executive Summary http://www.computereconomics.com/article.cfm?id=1090 [6] Virtual Machine http://en.wikipedia.org/wiki/Virtual_machine