Data Mining for Security Applications: Detecting Malicious Executables

1. Mr. Mehedy M. Masud (PhD Student) Prof. Latifur Khan Prof. Bhavani Thuraisingham Department of Computer Science The University of Texas at Dallas Data Mining for Security Applications:Detecting Malicious Executables

2. Outline and Acknowledgement • Vision for Assured Information Sharing • Handling Different Trust levels • Defensive Operations between Untrustworthy Partners • Detecting Malicious Executables using Data Mining • Research Funded by Air Force Office of Scientific Research and Texas Enterprise Funds

3. Vision: Assured Information Sharing Data/Policy for Coalition Publish Publish Data/Policy Data/Policy Publish Data/Policy Component Component Data/Policy for Data/Policy for Agency A Agency C • Trustworthy Partners • Semi-Trustworthy partners • Untrustworthy partners • Dynamic Trust Component Data/Policy for Agency B

4. Our Approach • Integrate the Medicaid claims data and mine the data; next enforce policies and determine how much information has been lost by enforcing policies • Prof. Khan, Dr. Awad (Postdoc) and Student Workers (MS students) • Apply game theory and probing techniques to extract information from semi-trustworthy partners • Prof. Murat Kantarcioglu and Ryan Layfield (PhD Student) • Data Mining for Defensive and offensive operations • E.g., Malicious code detection, Honeypots • Prof. Latifur Khan and Mehedy Masud • Dynamic Trust levels, Peer to Peer Communication • Prof. Kevin Hamlen and Nathalie Tsybulnik (PhD student)

5. Introduction: Detecting Malicious Executables using Data Mining • What are malicious executables? • Harm computer systems • Virus, Exploit, Denial of Service (DoS), Flooder, Sniffer, Spoofer, Trojan etc. • Exploits software vulnerability on a victim • May remotely infect other victims • Incurs great loss. Example: Code Red epidemic cost $2.6 Billion • Malicious code detection: Traditional approach • Signature based • Requires signatures to be generated by human experts • So, not effective against “zero day” attacks

6. State of the Art: Automated Detection • Automated detection approaches: • Behavioural: analyse behaviours like source, destination address, attachment type, statistical anomaly etc. • Content-based: analyse the content of the malicious executable • Autograph (H. Ah-Kim – CMU): Based on automated signature generation process • N-gram analysis (Maloof, M.A. et .al.): Based on mining features and using machine learning.

7. New Ideas • Content -based approaches consider only machine-codes (byte-codes). • Is it possible to consider higher-level source codes for malicious code detection? • Yes: Diassemble the binary executable and retrieve the assembly program • Extract important features from the assembly program • Combine with machine-code features

8. Feature Extraction • Binary n-gram features • Sequence of n consecutive bytes of binary executable • Assembly n-gram features • Sequence of n consecutive assembly instructions • System API call features • DLL function call information

9. The Hybrid Feature Retrieval Model • Collect training samples of normal and malicious executables. • Extract features • Train a Classifier and build a model • Test the model against test samples

10. Hybrid Feature Retrieval (HFR) • Training

11. Hybrid Feature Retrieval (HFR) • Testing

12. Feature Extraction • Binary n-gram features • Features are extracted from the byte codes in the form of n-grams, where n = 2,4,6,8,10 and so on. • Example: • Given a 11-byte sequence: 0123456789abcdef012345, • The 2-grams (2-byte sequences) are: 0123, 2345, 4567, 6789, 89ab, abcd, cdef, ef01, 0123, 2345 • The 4-grams (4-byte sequences) are: 01234567, 23456789, 456789ab,...,ef012345 and so on.... • Problem: • Large dataset. Too many features (millions!). • Solution: • Use secondary memory, efficient data structures • Apply feature selection

13. Feature Extraction • Assembly n-gram features • Features are extracted from the assembly programs in the form of n-grams, where n = 2,4,6,8,10 and so on. • Example: • three instructions • “push eax”; “mov eax, dword[0f34]” ; “add ecx, eax”; • 2-grams • (1) “push eax”; “mov eax, dword[0f34]”; • (2) “mov eax, dword[0f34]”; “add ecx, eax”; • Problem: • Same problem as binary • Solution: • Same solution

14. Feature Selection • Select Best K features • Selection Criteria: Information Gain • Gain of an attribute A on a collection of examples S is given by

15. Experiments • Dataset • Dataset1: 838 Malicious and 597 Benign executables • Dataset2: 1082 Malicious and 1370 Benign executables • Collected Malicious code from VX Heavens (http://vx.netlux.org) • Disassembly • Pedisassem (http://www.geocities.com/~sangcho/index.html ) • Training, Testing • Support Vector Machine (SVM) • C-Support Vector Classifiers with an RBF kernel

16. Results • HFS = Hybrid Feature Set • BFS = Binary Feature Set • AFS = Assembly Feature Set

17. Results • HFS = Hybrid Feature Set • BFS = Binary Feature Set • AFS = Assembly Feature Set

18. Results • HFS = Hybrid Feature Set • BFS = Binary Feature Set • AFS = Assembly Feature Set

19. Future Plans • System call: • seems to be very useful. • Need to Consider Frequency of call • Call sequence pattern (following program path) • Actions immediately preceding or after call • Detect Malicious code by program slicing • requires analysis

20. Data Mining to Detect Buffer Overflow Attack Mohammad M. Masud, Latifur Khan, Bhavani Thuraisingham Department of Computer Science The University of Texas at Dallas

21. Introduction • Goal • Intrusion detection. • e.g.: worm attack, buffer overflow attack. • Main Contribution • 'Worm' code detection by data mining coupled with 'reverse engineering'. • Buffer overflow detection by combining data mining with static analysis of assembly code.

22. Background • What is 'buffer overflow'? • A situation when a fixed sized buffer is overflown by a larger sized input. • How does it happen? • example: ........ char buff[100]; gets(buff); ........ memory buff Stack Input string

23. Background (cont...) • Then what? buff Stack ........ char buff[100]; gets(buff); ........ memory buff Stack Return address overwritten Attacker's code memory buff Stack New return address points to this memory location

24. Background (cont...) • So what? • Program may crash or • The attacker can execute his arbitrary code • It can now • Execute any system function • Communicate with some host and download some 'worm' code and install it! • Open a backdoor to take full control of the victim • How to stop it?

25. Background (cont...) • Stopping buffer overflow • Preventive approaches • Detection approaches • Preventive approaches • Finding bugs in source code. Problem: can only work when source code is available. • Compiler extension. Same problem. • OS/HW modification • Detection approaches • Capture code running symptoms. Problem: may require long running time. • Automatically generating signatures of buffer overflow attacks.

26. CodeBlocker (Our approach) • A detection approach • Based on the Observation: • Attack messages usually contain code while normal messages contain data. • Main Idea • Check whether message contains code • Problem to solve: • Distinguishing code from data

27. Severity of the problem • It is not easy to detect actual instruction sequence from a given string of bits

28. Our solution • Apply data mining. • Formulate the problem as a classification problem (code, data) • Collect a set of training examples, containing both instances • Train the data with a machine learning algorithm, get the model • Test this model against a new message

29. CodeBlocker Model

30. Feature Extraction

31. Disassembly • We apply SigFree tool • implemented by Xinran Wang et al. (PennState)

32. Feature extraction • Features are extracted using • N-gram analysis • Control flow analysis • N-gram analysis What is an n-gram? -Sequence of n instructions Traditional approach: -Flow of control is ignored 2-grams are: 02, 24, 46,...,CE Assembly program Corresponding IFG

33. Feature extraction (cont...) • Control-flow Based N-gram analysis What is an n-gram? -Sequence of n instructions Proposed Control-flow based approach -Flow of control is considered 2-grams are: 02, 24, 46,...,CE, E6 Assembly program Corresponding IFG

34. Feature extraction (cont...) • Control Flow analysis. Generated features • Invalid Memory Reference (IMR) • Undefined Register (UR) • Invalid Jump Target (IJT) • Checking IMR • A memory is referenced using register addressing and the register value is undefined • e.g.: mov ax, [dx + 5] • Checking UR • Check if the register value is set properly • Checking IJT • Check whether jump target does not violate instruction boundary

35. Feature extraction (cont...) • Why n-gram analysis? • Intuition: in general, disassembled executablesshould have a different pattern of instruction usage than disassembled data. • Why control flow analysis? • Intuition: there should be no invalid memory references or invalid jump targets.

36. Putting it together • Compute all possible n-grams • Select best k of them • Compute feature vector (binary vector) for each training example • Supply these vectors to the training algorithm

37. Experiments • Dataset • Real traces of normal messages • Real attack messages • Polymorphic shellcodes • Training, Testing • Support Vector Machine (SVM)

38. Results • CFBn: Control-Flow Based n-gram feature • CFF: Control-flow feature

39. Novelty / contribution • We introduce the notion of control flow based n-gram • We combine control flow analysis with data mining to detect code / data • Significant improvement over other methods (e.g. SigFree)

40. Advantages • 1) Fast testing • 2) Signature free operation 3) Low overhead • 4) Robust against many obfuscations

41. Limitations • Need samples of attack and normal messages. • May not be able to detect a completely new type of attack.

42. Future Works • Find more features • Apply dynamic analysis techniques • Semantic analysis

43. Reference / suggested readings • X. Wang, C. Pan, P. Liu, and S. Zhu. Sigfree: A signature free buffer overflow attack blocker. In USENIX Security, July 2006. • Kolter, J. Z., and Maloof, M. A. Learning to detect malicious executables in the wild Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining Seattle, WA, USA Pages: 470 – 478, 2004.

44. Email Worm Detection (behavioural approach) Outgoing Emails The Model Feature extraction Test data Machine Learning Training data Classifier Cleanor Infected ?

45. Feature Extraction Per email features • Binary valued Features Presence of HTML; script tags/attributes; embedded images; hyperlinks; Presence of binary, text attachments; MIME types of file attachments • Continuous-valued Features Number of attachments; Number of words/characters in the subject and body Per window features • Number of emails sent; Number of unique email recipients; Number of unique sender addresses; Average number of words/characters per subject, body; average word length:; Variance in number of words/characters per subject, body; Variance in word length • Ratio of emails with attachments

46. Feature Reduction & Selection Principal Component Analysis • Reduce higher dimensional data into lower dimension • Helps reducing noise, overfitting Decesion Tree • Used to Select Best features

47. Experiments • Data Set • Contains instances for both normal and viral emails. • Six worm types: • bagle.f, bubbleboy, mydoom.m, mydoom.u, netsky.d, sobig.f • Collected from UC Berkeley • Training, Testing: • Decision Tree: C4.5 algorithm (J48) on Weka Systems • Support Vector Machine (SVM) and Naïve Bayes (NB).

48. Results

49. Conclusion & Future Work • Three approaches has been tested • Apply classifier directly • Apply dimension reduction (PCA) and then classify • Apply feature selection (decision tree) and then classify • Decision tree has the best performance • Future Plans • Combine content based with behavioral approaches • Offensive Operations • Honeypots, Information operations