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Malicious Code Detection and Security Applications

Malicious Code Detection and Security Applications

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Malicious Code Detection and Security Applications

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  1. Malicious Code Detection and Security Applications Prof. Bhavani Thuraisingham The University of Texas at Dallas October 2008

  2. Information Harvesting Knowledge Mining Data Mining Knowledge Discovery in Databases Data Dredging Data Archaeology Data Pattern Processing Database Mining Knowledge Extraction Siftware The process of discovering meaningful new correlations, patterns, and trends by sifting through large amounts of data, often previously unknown, using pattern recognition technologies and statistical and mathematical techniques (Thuraisingham, Data Mining, CRC Press 1998) What is Data Mining?

  3. What’s going on in data mining? • What are the technologies for data mining? • Database management, data warehousing, machine learning, statistics, pattern recognition, visualization, parallel processing • What can data mining do for you? • Data mining outcomes: Classification, Clustering, Association, Anomaly detection, Prediction, Estimation, . . . • How do you carry out data mining? • Data mining techniques: Decision trees, Neural networks, Market-basket analysis, Link analysis, Genetic algorithms, . . . • What is the current status? • Many commercial products mine relational databases • What are some of the challenges? • Mining unstructured data, extracting useful patterns, web mining, Data mining, security and privacy

  4. Data Mining for Intrusion Detection: Problem • An intrusion can be defined as “any set of actions that attempt to compromise the integrity, confidentiality, or availability of a resource”. • Attacks are: • Host-based attacks • Network-based attacks • Intrusion detection systems are split into two groups: • Anomaly detection systems • Misuse detection systems • Use audit logs • Capture all activities in network and hosts. • But the amount of data is huge!

  5. Our Approach: Overview Training Data Class Hierarchical Clustering (DGSOT) SVM Class Training Testing DGSOT: Dynamically growing self organizing tree Testing Data

  6. Our Approach: Hierarchical Clustering Our Approach Hierarchical clustering with SVM flow chart

  7. Results Training Time, FP and FN Rates of Various Methods

  8. 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

  9. State of the Art in 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.

  10. Our New Ideas (Khan, Masud and Thuraisingham) • 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

  11. 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

  12. 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

  13. Hybrid Feature Retrieval (HFR) • Training

  14. Hybrid Feature Retrieval (HFR) • Testing

  15. 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

  16. 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

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

  18. Experiments • Dataset • Dataset1: 838 Malicious and 597 Benign executables • Dataset2: 1082 Malicious and 1370 Benign executables • Collected Malicious code from VX Heavens ( • Disassembly • Pedisassem ( ) • Training, Testing • Support Vector Machine (SVM) • C-Support Vector Classifiers with an RBF kernel

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

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

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

  22. 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

  23. Data Mining for Buffer Overflow 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.

  24. 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

  25. 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

  26. 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?

  27. 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.

  28. 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

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

  30. 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

  31. CodeBlocker Model

  32. Feature Extraction

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

  34. 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

  35. 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

  36. 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

  37. Putting it together • 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. • Approach • 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

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

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

  40. Novelty, Advantages, Limitations, Future • Novelty • 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) • Advantages • Fast testing • Signature free operation • Low overhead • Robust against many obfuscations • Limitations • Need samples of attack and normal messages. • May not be able to detect a completely new type of attack. • Future • Find more features • Apply dynamic analysis techniques • Semantic analysis

  41. Analysis of Firewall Policy Rules Using Data Mining Techniques • Firewall is the de facto core technology of today’s network security • First line of defense against external network attacks and threats • Firewall controls or governs network access by allowing or denying the incoming or outgoing network traffic according to firewall policy rules. • Manual definition of rules often result in in anomalies in the policy • Detecting and resolving these anomalies manually is a tedious and an error prone task • Solutions: • Anomaly detection: • Theoretical Framework for the resolution of anomaly; • A new algorithm will simultaneously detect and resolve any anomaly that is present in the policy rules • Traffic Mining: Mine the traffic and detect anomalies

  42. Firewall Log File Mining Log File Using Frequency Filtering Rule Generalization Edit Firewall Rules Identify Decaying & Dominant Rules Generic Rules Traffic Mining • To bridge the gap between what is written in the firewall policy rules and what is being observed in the network is to analyze traffic and log of the packets– traffic mining • Network traffic trend may show that some rules are out-dated or not used recently Firewall Policy Rule

  43. 1: TCP,INPUT,,ANY,*.*.*.*,80,DENY 2: TCP,INPUT,*.*.*.*,ANY,*.*.*.*,80,ACCEPT 3: TCP,INPUT,*.*.*.*,ANY,*.*.*.*,443,DENY 4: TCP,INPUT,,ANY,*.*.*.*,22,DENY 5: TCP,INPUT,*.*.*.*,ANY,*.*.*.*,22,ACCEPT 6: TCP,OUTPUT,,ANY,*.*.*.*,22,DENY 7: UDP,OUTPUT,*.*.*.*,ANY,*.*.*.*,53,ACCEPT 8: UDP,INPUT,*.*.*.*,53,*.*.*.*,ANY,ACCEPT 9: UDP,OUTPUT,*.*.*.*,ANY,*.*.*.*,ANY,DENY 10: UDP,INPUT,*.*.*.*,ANY,*.*.*.*,ANY,DENY 11: TCP,INPUT,,ANY,,22,DENY 12: TCP,INPUT,,ANY,,80,DENY 13: UDP,INPUT,*.*.*.*,ANY,,ANY,DENY 14: UDP,OUTPUT,,ANY,129.110.10.*,ANY,DENY 15: TCP,INPUT,*.*.*.*,ANY,,22,ACCEPT 16: TCP,INPUT,*.*.*.*,ANY,,80,ACCEPT 17: UDP,INPUT,129.110.*.*,53,,ANY,ACCEPT 18: UDP,OUTPUT,,ANY,129.110.*.*,53,ACCEPT Traffic Mining Results Rule 1, Rule 2: ==> GENRERALIZATION Rule 1, Rule 16: ==> CORRELATED Rule 2, Rule 12: ==> SHADOWED Rule 4, Rule 5: ==> GENRERALIZATION Rule 4, Rule 15: ==> CORRELATED Rule 5, Rule 11: ==> SHADOWED Anomaly Discovery Result

  44. Worm Detection: Introduction • What are worms? • Self-replicating program; Exploits software vulnerability on a victim; Remotely infects other victims • Evil worms • Severe effect; Code Red epidemic cost $2.6 Billion • Goals of worm detection • Real-time detection • Issues • Substantial Volume of Identical Traffic, Random Probing • Methods for worm detection • Count number of sources/destinations; Count number of failed connection attempts • Worm Types • Email worms, Instant Messaging worms, Internet worms, IRC worms, File-sharing Networks worms • Automatic signature generation possible • EarlyBird System (S. Singh -UCSD); Autograph (H. Ah-Kim - CMU)

  45. Email Worm Detection using Data Mining • Task: • given some training instances of both “normal” and “viral” emails, • induce a hypothesis to detect “viral” emails. • We used: • Naïve Bayes • SVM Outgoing Emails The Model Test data Feature extraction Classifier Machine Learning Training data Cleanor Infected ?

  46. Assumptions • Features are based on outgoing emails. • Different users have different “normal” behaviour. • Analysis should be per-user basis. • Two groups of features • Per email (#of attachments, HTML in body, text/binary attachments) • Per window (mean words in body, variable words in subject) • Total of 24 features identified • Goal: Identify “normal” and “viral” emails based on these features

  47. Feature sets • 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

  48. Data Mining Approach Classifier Clean/ Infected Test instance Clean/ Infected infected? SVM Naïve Bayes Test instance Clean? Clean

  49. Data set • Collected from UC Berkeley. • Contains instances for both normal and viral emails. • Six worm types: • bagle.f, bubbleboy, mydoom.m, • mydoom.u, netsky.d, sobig.f • Originally Six sets of data: • training instances: normal (400) + five worms (5x200) • testing instances: normal (1200) + the sixth worm (200) • Problem: Not balanced, no cross validation reported • Solution: re-arrange the data and apply cross-validation

  50. Our Implementation and Analysis • Implementation • Naïve Bayes: Assume “Normal” distribution of numeric and real data; smoothing applied • SVM: with the parameter settings: one-class SVM with the radial basis function using “gamma” = 0.015 and “nu” = 0.1. • Analysis • NB alone performs better than other techniques • SVM alone also performs better if parameters are set correctly • mydoom.m and VBS.Bubbleboy data set are not sufficient (very low detection accuracy in all classifiers) • The feature-based approach seems to be useful only when we have • identified the relevant features • gathered enough training data • Implement classifiers with best parameter settings