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1 Monthly Research Consideration and evaluation of using fuzzy hashing Fourteenforty Research Institute, Inc. FFRI, Inc Ver
2 Agenda Background and purpose Basis of fuzzy hashing An experiment The result Consideration 2
3 Background and purpose fuzzy hashing was introduced in 2006 by Jesse Kornblum In malware analysis fuzzy hashing algorithms such as ssdeep are being introduced in recent years (IMHO) However, we don t consider the effective usage of them enough In this slides, we evaluate an effectiveness of classification of malware similarity by fuzzy hashing 3
4 Basis of Fuzzy hashing(1/4) In general, cryptographic hashing like MD5 is popular and it has the attributes as follows: (cf. it is easy to compute the hash value for any given message it is infeasible to generate a message that has a given hash it is infeasible to modify a message without changing the hash it is infeasible to find two different messages with the same hash Cryptographic hashing is often used for identify the same files On the other hand, it is unsuitable to identify similar files because digests are completely different even if 1 bit is altered in the other file In DFIR, this need exists and fuzzy hashing was developed to solve this problem 4
5 Basis of Fuzzy hashing(2/4) Fuzzy hashing = Context Triggered Piecewise Hashing(CTPH) = Piecewise hashing + Rolling hashing Piecewise Hashing Dividing message into N-block and calculating hash value of each blocks Rolling Hash A method to calculate hash value of sub-message(position 1-3, 2-4 ) fast In general, if calculated values are the same, it is a high probability that original messages are identical a b c d e f g h Calculating a hash value for each N chars(n=3) 5
6 Basis of Fuzzy hashing(3/4) Fuzzy hashing(ctph) When rolling hash generates a specific value at any position, it calculates cryptographic hash value of the partial message from the beginning to the position Generate a hash value by concatenating all (partial) hashes a a a b b b c c 1calculating rolling hash, and the result is the specific value (trigger) 2calculating a hash value of this block by cryptographic hash 3calculating rolling hash value from position 4(trying to determine a next block) 6
7 Basis of Fuzzy hashing(4/4) Fuzzy hashing(ctph) With almost identical messages, it would calculate a hash value of identical partial message stochastically We can identify partial matches between similar messages a a a b b b c c 238c7d Hash values for each block a c 817d 928c a Hash value for entire message b b b a a a d d 8c2372 7
8 An experiment(1/2) In general, usage of fuzzy hashing is proposed as follows: Determining similar files(i.e. almost identical but MD5s aren t matched) Matching partial data in files This time, we evaluate determining similar malware by fuzzy hashing We make it clear how effective it is in actually and what we should consider if we applying it for the above 8
9 An experiment(2/2) Preparation Preparing 2,036 unique malware files in MD5s collected by ourselves Calculating(fuzzy) hash values by ssdeep and similarity of all of each files ncr: 2,036 C 2 = 2,071,630 combinations Determining similar malware Extracting all the pairs whose similarities are 50%-100% Determining if the detection name of files in a pair is matched for each similarity threshold similarity of each malware files(%) A B C A B %+ A-B A-C 80%+ (A)Trojan.XYZ (B)Trojan.XYX (A)Trojan.XYZ (C)WORM.DEA Matched Not matched C 46 9
10 The number of pairs whose similarity are above threshold FFRI,Inc. The result The higher the threshold is, the higher matching rate of detection name we get Up to the threshold of 90% it keeps around 50-60% matching rate Threshold of similarity(%) 10
11 The number of pairs whose similarity are above threshold FFRI,Inc. The rate detected by the name Generic? Dividing matched pairs into a group who has generic in its name and the others matched(generic)% shows the same trend with the matching rate above -> The higher the threshold is, the more malware are detected as generic Threshold of similarity(%) 11
12 Consideration A meaning of the result depends on if the AVV uses fuzzy hashing for generic detection If they use fuzzy hashing for generic detection If not The result is natural By using fuzzy hashing, we may obtain a similar result to the generic detection If we use fuzzy hashing for generic detection, 90%+ similarity might be required with known malware (fuzzy) hash values 12
13 Contact Information 13
Fourteenforty Research Institute, Inc.
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