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HUNTING FOR METAMORPHIC ENGINES

HUNTING FOR METAMORPHIC ENGINES. Mark Stamp & Wing Wong September 13, 2006. Outline. Metamorphic software Both good and evil uses Metamorphic virus construction kits How effective are metamorphic engines? How to compare two pieces of code? Similarity within and between virus families

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HUNTING FOR METAMORPHIC ENGINES

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  1. HUNTING FOR METAMORPHIC ENGINES Mark Stamp & Wing Wong September 13, 2006

  2. Outline • Metamorphic software • Both good and evil uses • Metamorphic virus construction kits • How effective are metamorphic engines? • How to compare two pieces of code? • Similarity within and between virus families • Similarity to non-viral code • Can we detect metamorphic viruses? • Commercial virus scanners • Hidden Markov models (HMMs) • Similarity index • Conclusion

  3. PART I Metamorphic Software

  4. What is Metamorphic Software? • Software is metamorphic provided • All copies do the same thing • Internal structure of copies differs • Today almost all software is cloned • “Good” metamorphic software… • Mitigate buffer overflow attacks • “Bad” metamorphic software… • Avoid virus/worm signature detection

  5. Metamorphic Software for Good? • Suppose program has a buffer overflow • If we clone the program • One attack breaks every copy • Break once, break everywhere (BOBE) • If instead, we have metamorphic copies • Each copy still has a buffer overflow • One attack does not work against every copy • BOBE-resistant • Analogous to genetic diversity in biology • A little metamorphism does a lot of good!

  6. Metamorphic Software for Evil? • Cloned virus/worm can be detected • Common signature on every copy • Detect once, detect everywhere (DODE?) • If instead virus/worm is metamorphic • Each copy has different signature • Same detection does not work against every copy • Provides DODE-resistance • Analogous to genetic diversity in biology • But, effective use of metamorphism here is tricky!

  7. Crypto Analogy • In information security, almost everything that consistently works is either • Crypto, or • Has a crypto analogy… • Consider WWII ciphers • German Enigma • Broken by Polish and British cryptanalysts • Design was (mostly) known to cryptanalysts • Japanese Purple • Broken by American cryptanalysts • Design was (mostly) unknown to cryptanalysts

  8. Crypto Analogy • Cryptanalysis  break a (known) cipher • Diagnosis  determine how an unknown cipher works (from ciphertext) • Which was the greater achievement, breaking Enigma or Purple? • Cryptanalysis of Enigma was harder • Diagnosis of Purple was harder • Can make a reasonable case for either…

  9. Crypto Analogy • What does this have to do with metamorphic software? • Suppose we (the good guys) generate metamorphic copies of our software • Bad guys can attack individual copies • Can bad guys attack all copies? • Bad guys can try to diagnose our metamorphic generator

  10. Crypto Analogy • How to diagnose metamorphic generator (from exe’s)? • Reverse engineer many copies, look at differences, etc., etc. • Lots of work • Diagnosis problem is hard • If good guys can force bad guys to solve a diagnosis problem, the good guys win • Security by obscurity? Violates (spirit of) Kerckhoffs’ Principle? • Yes, but still may be valuable in the real world

  11. Crypto Analogy • What about case where bad guys write metamorphic code? • Metamorphic viruses, for example • Do good guys need to solve diagnosis problem? • If so, good guys are in trouble • Not if good guys “only” need to detect the metamorphic code (not diagnose) • Not claiming the good guys job is easy • Just claiming that there is hope…

  12. Virus Evolution • Viruses first appeared in the 1980s • Fred Cohen • Viruses must avoid signature detection • Virus can alter its “appearance” • Techniques employed • encryption • polymorphic • metamorphic

  13. Virus Evolution - Encryption • Virus consists of • decrypting module (decryptor) • encrypted virus body • Different encryption key • different virus body signature • Weakness • decryptor can be detected

  14. Virus Evolution – Polymorphism • Try to hide signature of decryptor • Can use code emulator to decrypt putative virus dynamically • Decrypted virus body is constant • Signature detection is possible

  15. Virus Evolution – Metamorphism • Change virus body • Mutation techniques: • permutation of subroutines • insertion of garbage/jump instructions • substitution of instructions

  16. PART II Virus Construction Kits

  17. Virus Construction Kits – PS-MPC • According to Peter Szor: “… PS-MPC [Phalcon/Skism Mass-Produced Code generator] uses a generator that effectively works as a code-morphing engine…… the viruses that PS-MPC generates are not [only] polymorphic, but their decryption routines and structures changein variants…”

  18. Virus Construction Kits – G2 • From the documentation of G2 (Second Generation virus generator): “… different viruses may be generated from identical configuration files…”

  19. Virus Construction Kits - NGVCK • From the documentation for NGVCK(Next Generation Virus Creation Kit): “… all created viruses are completely different in structure and opcode…… impossible to catch all variants with one or more scanstrings.…… nearly 100% variability of the entire code” • Oh, really?

  20. PART III How Effective Are Metamorphic Engines?

  21. How We Compare Two Pieces of Code

  22. Virus Families – Test Data • Four generators, 45 viruses • 20 viruses by NGVCK • 10 viruses by G2 • 10 viruses by VCL32 • 5 viruses by MPCGEN • 20 normal utility programs from the Cygwin bin directory

  23. Similarity within Virus Families – Results

  24. Similarity within Virus Families – Results

  25. Similarity within Virus Families – Results

  26. Similarity within Virus Families – Results

  27. Similarity within Virus Families – Results

  28. NGVCK Similarity to Virus Families • NGVCK versus other viruses • 0% similar to G2 and MPCGEN viruses • 0 – 5.5% similar to VCL32 viruses (43 out of 100 comparisons have score > 0) • 0 – 1.2% similar to normal files (only 8 out of 400 comparisons have score > 0)

  29. NGVCK Metamorphism/Similarity • NGVCK • By far the highest degree of metamorphism of any kit tested • Virtually no similarity to other viruses or normal programs • Undetectable???

  30. PART IV Can Metamorphic Viruses Be Detected?

  31. Commercial Virus Scanners • Tested three virus scanners • eTrust version 7.0.405 • avast! antivirus version 4.7 • AVG Anti-Virus version 7.1 • Each scanned 37 files • 10 NGVCK viruses • 10 G2 viruses • 10 VCL32 viruses • 7 MPCGEN viruses

  32. Commercial Virus Scanners • Results • eTrust and avast! detected 17 (G2 and MPCGEN) • AVG detected 27 viruses (G2, MPCGEN and VCL32) • none of NGVCK viruses detected by the scanners tested

  33. Hidden Markov Models (HMMs) • state machines • transitions between states have fixed probabilities • each state has a probability distribution for observing a set of observation symbols • can “train” an HMM to represent a set of data (in the form of observation sequences) • states = features of the input data • transition and the observation probabilities = statistical properties of features

  34. HMM Example – the Occasionally Dishonest Casino

  35. HMM Example – the Occasionally Dishonest Casino • 2 states: fair/loaded • The switch between dice is a Markov process • Outcomes of a roll have different probabilities in each state • If we can only see a sequence of rolls, the state sequence is hidden • want to understand the underlying Markov process from the observations

  36. HMMs – the Three Problems • Find the likelihood of seeing an observation sequence O given a model , i.e. P(O | ) • Find an optimal state sequence that could have generated a sequence O • Find the model parameters given a sequence O, i.e. find transition and observation probabilities that maximize the probability of observing O • There exist efficient algorithms to solve the three problems

  37. HMM Application – Determining the Properties of English Text • Given: a large quantity of written English text • Input: a long sequence of observations consisting of 27 symbols (the 26 lower-case letters and the word space) • Train a model to find the most probable parameters (i.e., solve Problem 3) • Use trained model to score any unknown sequence of letters (and spaces) to determine whether it corresponds to English text. (i.e., solve Problem 1)

  38. HMM Application – Initial and Final Observation Probability Distributions

  39. HMM Application - Results • Observation probabilities converged, each letter belongs to one of the two hidden states • The two states correspond to consonants and vowels • Note: • no a priori assumption was made • HMM effectively recovered the statistically significant feature inherent in English

  40. HMM Application - Results • Probabilities can be sensibly interpreted for up to n = 12 hidden states • Trained model could be used to detect English text, even if the text is “disguised” by, say, a simple substitution cipher or similar transformation

  41. Virus Detection with HMMs • Use hidden Markov models (HMMs) to represent statistical properties of a set of metamorphic virus variants • Train the model on family of metamorphic viruses • Use trained model to determine whether a given program is similar to the viruses the HMM represents

  42. Virus Detection with HMMs • A trained HMM • maximizes the probabilities of observing the training sequence • assigns high probabilities to sequences similar to the training sequence • represents the “average” behavior if trained on multiple sequences • represents an entire virus family, as opposed to individual viruses

  43. Virus Detection with HMMs – Data • Data set • 200 NGVCK viruses (160 for training, 40 for testing) • Comparison set • 40 normal exes from Cygwin • 25 other “non-family” viruses (G2, MPCGEN and VCL32) • 25 HMM models generated and tested

  44. Virus Detection with HMMs – Methodology

  45. Virus Detection with HMMs – Results

  46. Virus Detection with HMMs – Results • Detect some other viruses “for free”

  47. Virus Detection with HMMs • Summary of experimental results • All normal programs distinguished • VCL32 viruses had scores close to NGVCK family viruses • With proper threshold, 17 HMM models had 100% detection rate and 10 models had 0% false positive rate • No significant difference in performance between HMMs with 3 or more hidden states

  48. Virus Detection with HMMs – Trained Models • Converged probabilities in HMM matrices may give insight into the features of the represented viruses • We observe • opcodes grouped into “hidden” states • most opcodes in one state only • What does this mean? • We are not sure…

  49. HMMs – The Trained Models

  50. Detection via Similarity Index • Straightforward similarity indexcan be used as detector • To determine whether a program belongs to the NGVCK virus family, compare it to any randomly chosen NGVCK virus • NGVCK similarity to non-NGVCK code is small • Can use this fact to detect metamorphic NGVCK variants

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