==Phrack Inc.== Volume 0x0e, Issue 0x44, Phile #0x0f of 0x13 |=-----------------------------------------------------------------------=| |=------------------=[ Similarities for Fun & Profit ]=------------------=| |=-----------------------------------------------------------------------=| |=---------------=[ Pouik (Androguard Team) and G0rfi3ld ]=--------------=| |=------------------=[ d@t0t0.fr / g0rfi3ld@gmail.com ]=-----------------=| |=-----------------------------------------------------------------------=| 1/ Introduction 1.1 Complexity of a sequence 1.2 Histograms and classical Shannon Entropy 1.3 From the Classical Entropy towards Descriptional Entropy 1.4 Normalized Compression Distance (NCD) 2/ Similarities 2.1 Between two sets of elements 2.2 In a set of elements 3/ Real World: Android 3.1 Similarities between two applications 3.2 Differences between two applications 3.3 Looking for a signature in applications 4/ Conclusion 5/ References 6/ Code --[ 1 - Introduction How can we verify that two numerical objects are identical? It's easy, you just have to compare all characters, one by one. But how can we say that two numerical objects are "similar" but not identical? Can we define a measure of "similarity", which will give ipso facto a measure of "dissimilarity"? But what are these numerical files that we want to analyze or compare? It could be anything, from pictures to numerical data files. We will focus in this work on goodware and malware, (a goodware is not a malware :). So, if the numerical objects are software, can we define a measure of similarity and how? And why? We will see this. Our problem can be simply defined as: How can we choose quickly, from a set M of known software files {m1, ..., mn}, with n >= 1, the subset of the files of M that are the "most similar" to a target A? And how can we find quickly interesting differences without using a direct approach like graph isomorphism [21, 25] between two similar but different applications? We will show you how we can use a filtering tactic to select the best (i.e. the "most similar" to a target A) files out of the malware set M. We propose the use of two different tactics, using the entropy as a first filtering tactic to filter the set M and the Normalized Compression Distance (NCD) for a second filtering tactic. We also propose a new entropy which is a simple generalization of the classical Shannon entropy. We call this entropy the "descriptional entropy", which to the authors knowledge, is presented here for the first time. While the tools that we present here are truly generic, i.e. they can be used with any files, we will give some examples through the analysis and comparison of Android applications. ----[ 1.1 Complexity of a sequence We want to compare DNA sequences , music files or pictures . We need a notion of the "complexity" of a sequence, to be able to compare them, to sort them or to index them. But what is a complex sequence or how do we define a complex sequence? There are lots of situations where we need a tool to answer. To be more exact, we need a computable measure of the complexity of a sequence, to index for example a set of files. The sequence can be the bytes of a picture, a DNA sequence, a source code or an executable file; in other words, whatever can be stored in a file. In this paper, we will say sequence, for any sequence of ASCII characters. So, can we define the "complexity" of a sequence? Let us give a toy example, we consider the four sequences: - S1 = "aaabbb" - S2 = "ababab" - S3 = "bbbaaa" - S4 = "abbaab" Intuition tells us that: - S1 and S3 are more similar than S1 and S2 or S2 and S3. - S1 is more "simple" than S2. - S1, S2 and S3 are more "simple" than S4. It is easy to see that S1 is the reverse of S3, so it could be interesting for any function Comp() defined as a measure of the complexity of a sequence to verify that Comp(S1) = Comp(S3). ----[ 1.2 Histograms and classical Shannon Entropy Let S be a sequence of characters, with an "alphabet" of n different symbols (generally characters). Let pi be the computed probability of occurrence of each of the n character in S, we will call the histogram vector Hist = {p1, ..., pn} and then the classical Shannon entropy of the sequence S is defined by: n __ \ H(S)= - / pi log(pi) |__ i=1 (where log(x) is the logarithmic function in base 10). In our toy example, S1 = "aaabbb", S2 = "ababba" and S3 = "bbbaaa", the alphabet is {"a", "b"}. They have a same histogram vector entropy: Hist(S1) = Hist(S2) = Hist(S3) = {1/2, 1/2} which will give the same entropy: H(S1) = H(S2) = H(S3) = 1. If we use the classical Shannon entropy H(), the equation holds as H(S1) = H(S3). However we also have H(S1) = H(S2) which contradicts 'S1 is more simple than S2'. So the function is not suitable. Let's see another problem with the classical Shannon entropy: if S is a sequence of characters with S...S a concatenation of S and H() the Shannon entropy, then we have H(S) = H(SS) = H(SSS) = H(S...S). This is not really good for our purposes! We will see that we can do better with a generalization of the Shannon entropy which we will call "Descriptional Entropy". ----[ 1.3 From the Classical Shannon Entropy towards the Descriptional Entropy A lot of ways to measure the complexity of a sequence have been proposed. For example, the Lempel-Ziv complexity [29,30] is defined as the number of different subsequences (patterns) in a sequence when we apply the LZ algorithm. The sequence complexity, or the complexity index, of a sequence S = s1...sn is defined as the number of different subsequences in S [31,32]. In all cases we obtain a number which is difficult to use, or we have to take the histogram vector. But to compare two histogram vectors of unequal size is not easy. We propose here a new approach. Given a complexity measure based on the count of different subsequences, and if we have N different subsequences, we can compute the histogram vector Hist(S) = {P1, ..., PN} for this set, with P1+...PN=1 of course. So now we can compute the entropy of this histogram vector; we propose to call this entropy the "Descriptional Entropy" of a sequence: N __ \ Hd(Hist(S))= - / Pi log(Pi) |__ i=1 To simplify we will write Hd(S) for Hd(Hist(S)). From now we will use the log2(x) function, i.e. the log base 2 function. Let us show it with the toy example, again, S1 = "aaabbb", S2 = "ababba", S3 = "bbbaaa" and S4 = "abbaab". If we choose to count all different subsequences we will have (to simplify we neglect the "empty" sequence which is used sometimes): (1) For S1 = "aaabbb": the subsequence set is (in alphabetical order) {a,aa,aaa,aaab,aaabb,aaabbb,aab,aabb,aabbb,ab,abb,abbb,b,bb,bbb} and the histogram vector: Hist(S1)={1/7,2/21,1/21,1/21,1/21,1/21,1/21,1/21,1/21,1/21,1/21,1/21, 1/7,2/21,1/21}. If we sort it we have: {1/21,1/21,1/21,1/21,1/21,1/21,1/21,1/21,1/21,1/21,1/21,2/21,2/21, 1/7,1/7} and so the descriptional entropy will be (remember: we use the base 2 logarithmic function log2(x)): Hd(S1) = - ( 1/21 log2(1/21) x 11 + 2/21 log2(2/21) x 2 + 1/7 log2(1/7) x 2 ) Hd(S1) = 11/21 log2(21) + 4/21 log2(21/2) + 2/7 log2(7) Hd(S1) = 11/21 log2(21) + 4/21 log2(21) - 4/21 log2(2) + 2/7 log2(7) Hd(S1) = 5/7 log2(21) - 4/21 log2(2) + 2/7 log2(7) which gives: Hd(S1) = 3.74899 (2) For S2 = "ababab": the subsequence set is (in alphabetical order): {a,ab,aba,abab,ababa,ababab,b,ba,bab,baba,babab} and the histogram vector: Hist(S2)= {1/7,1/7,2/21,2/21,1/21,1/21,1/7,2/21,2/21,1/21,1/21} If we sort it we have: {1/21,1/21,1/21,1/21,2/21,2/21,2/21,2/21,1/7,1/7,1/7} and the descriptional entropy will be: Hd(S2) = 3 log2(7) / 7 + 8 log2(21/2) / 21 + 4 log2(21) / 21 which gives: Hd(S2) = 3.3321 (3) For S3 = "bbbaaa": the subsequence set is (in alphabetical order) {a,aa,aaa,aaab,aaabb,aaabbb,aab,aabb,aabbb,ab,abb,abbb,b,bb,bbb} and the histogram vector: Hist(S3)={1/7,2/21,1/21,1/7,1/21,1/21,1/21,2/21,1/21,1/21,1/21, 1/21,1/21,1/21,1/21} If components are sorted we have: {1/21,1/21,1/21,1/21,1/21,1/21,1/21,1/21,1/21,1/21,1/21,2/21,2/21, 1/7,1/7} and the descriptional entropy will be: Hd(S3) = 2 log2(7))/7 + 4 log2(21/2))/ 21 + 11 log2(21)/21 which gives: Hd(S3) = 3.74899 (4) For S4 = "abbaab": the subsequence set is (in alphabetical order) {a,aa,aab,ab,abb,abba,abbaa,abbaab,b,ba,baa,baab,bb,bba,bbaa,bbaab} and the histogram vector: Hist(S4) = {1/7,1/21,1/21,2/21,1/21,1/21,1/21,1/21,1/7,1/21, 1/21,1/21,1/21,1/21,1/21,1/21} If components are sorted we have: {1/21,1/21,1/21,1/21,1/21,1/21,1/21,1/21,1/21,1/21,1/21,1/21,1/21, 2/21,1/7,1/7} and the descriptional entropy will be: Hd(S4) = 2 log2(7) / 7 + 2 log2(21/2) / 21 + 13 log(21) / 21 which gives: Hd(S4) = 3.84423 So, we have: Hd(S2) = 3.3321 < Hd(S1) = Hd(S3) = 3.74899 < Hd(S4) = 3.84423. The result Hd(S1) = Hd(S3) = 3.74899 is expected. However, the result Hd(S2) = 3.3321 < Hd(S1) is a little bit surprising, but the whole set of inequalities is correct. S4 is more "complex" than S1, S2 and S3. Let us give another simple example, if we choose S5 = "bbbbbaaaaa" and S6 = S5S5 = "bbbbbaaaaabbbbbaaaaa". We will have: Hd(S5) = 4.82265 and Hd(S6) = 6.68825, and it is not so difficult to prove that: for any sequence S: Hd(S) < Hd(SS) < Hd(SSS) < Hd(S......S). This sounds good :) However there is a drawback since for a very long sequence S the (practical) computational complexity of the computation of Hd(S) is not cheap. Well, it's true, of course. We could probably find a fast(er) algorithm, based for example on some variation of the Aho-Corasick, Boyer-Moore or Knuth-Morris-Pratt algorithms. We can also say that we only consider subsequences of length bounded by a suitable integer k. ----[ 1.4 Normalized Compression Distance (NCD) The Kolmogorov complexity is a very interesting concept and it has a lot of applications [18,23]. We present here this concept to explain the power of Normalized Compression Distance (NCD). Let us cite Wikipedia : "In algorithmic information theory (a subfield of computer science), the Kolmogorov complexity of an object, such as a piece of text, is a measure of the computational resources needed to specify the object. It is named after Soviet Russian mathematician Andrey Kolmogorov. Kolmogorov complexity is also known as descriptive complexity, Kolmogorov Chaitin complexity, algorithmic entropy, or program-size complexity." Well, it is a very good abstract. Unfortunately, The Kolmogorov complexity K(S) of a sequence S is not computable, so we can just approximate it. The use of any compression algorithm gives a trivial and evident upper bound of K(S). Read the book  for a deep and modern presentation and for applications. (Yes there are applications). We switch now to the NCD, which is always computable. To be able to use the Kolmogorov complexity we need to extend the informational distance to have a normalized value which indicates the similarities or dissimilarities between two elements/strings. Let us recall what a distance is. Wikipedia says: "...a distance function on a given set M is a function d: MxM -> R, the set of real numbers, that satisfies the following conditions: a) d(x,y) >= 0, and d(x,y) = 0 if and only if x = y. (Distance is positive between two different points, and is zero precisely from a point to itself.) b) It is symmetric: d(x,y) = d(y,x). (The distance between x and y is the same in either direction.) c) It satisfies the triangle inequality: d(x,z) <= d(x,y) + d(y,z). (The distance between two points is the shortest distance along any path). Such a distance function is known as a metric." Suppose we have two sequences x and y. We consider the concatenated sequence xy, and a compressor algorithm Comp with L(Comp(S)) the length of the compressed string, i.e. the number of bytes of the compressed string. The main idea of the NCD [17,33] is that L(Comp(xy)) will be almost equal to L(Comp(x)) if x = y. And L(Comp(xy)) will be close to L(Comp(x)) if x and y are similar without being equal. Let us give now the definition of dNCD(x,y): (L(Comp(x|y)) - min{L(Comp(x)), L(Comp(y))}) - dNCD(x, y) = ------------------------------------------------- max{L(Comp(x)), L(Comp(y))} This formula returns a value from 0.0 (maximally similar) to 1.0 (maximally dissimilar). 1.0? Oh yes if the compressor works correctly, but practically we can manage this. --[ 2 - Similarities In the next two sections, we will present two algorithms which can be used with any set of elements. Elsim (included in the code archive at the end of this paper) is our implementation of those algorithms. It is open source software (LGPL), and with it you can compute similarities between any "described" elements :) ----[ 2.1 Between 2 sets of elements In this part we will describe how we can create a generic algorithm to search the similarities between 2 sets of elements. This algorithm can be used to compare all kind of elements if you are able to find a correct way to represent your data. For the comparison of our data, we will use the NCD, thus indirectly the Kolmogorov complexity. One of the major drawbacks  of the compression is the "time" required :) The tool can be powerful, but if you use a compression algorithm like LZMA, you are limited to "hello world" problems due to the speed of the compression. You need to choose carefully your (lossless) compressor. A compressor C is "normal" if the following properties (inequalities) are satisfied : 1) Idempotency: C(xx) = C(x), and C(E) = 0, where E is the empty string. 2) Monotonicity: C(xy) >= C(x). 3) Symmetry: C(xy) = C(yx). 4) Distributivity: C(xy) + C(z) <= C(xz) + C(yz). The important theorem from  reveals the power of the dNCD distance: "if the compressor C is normal, then the dNCD is a normalized admissible distance satisfying the metric inequalities that is, a similarity metric." With this theorem, we can use the NCD as a simple tool to measure the similarity between elements. We have performed tests on different compressors to test them in respect to both the previous properties and their speed. If the algorithm is too slow it will be really useless for practical purposes. We chose to compress random signatures (see 3.1) because it's close to our application domain (see 3). ########################################################################## Property | Number of success | size of final compression | speed (seconds) ########################################################################## d@t0t0:~/elsim$./tests/test_similarity.py * LZMA Idempotency 0/9 1167 1.82118797 Monotonicity 72/72 13258 9.40736294 Symetry 72/72 17380 9.32561111 Distributivity 504/504 214466 133.67427087 * BZ2 Idempotency 0/9 1947 0.00075889 Monotonicity 72/72 18248 0.00626206 Symetry 72/72 221744 0.00735211 Distributivity 504/504 279944 0.09816098 * ZLIB Idempotency 0/9 1073 0.00033116 Monotonicity 72/72 11850 0.00224590 Symetry 72/72 15348 0.00276113 Distributivity 504/504 190386 0.03468490 * XZ Idempotency 0/9 1900 0.55278206 Monotonicity 72/72 17544 4.41346812 Symetry 72/72 21008 4.35566306 Distributivity 504/504 269864 61.70975709 * VCBLOCKSORT Idempotency 0/9 8129 0.00140786 Monotonicity 72/72 86960 0.01695490 Symetry 10/72 115168 0.02190304 Distributivity 504/504 1414896 0.21149492 * SNAPPY Idempotency 0/9 1153 0.00009203 Monotonicity 72/72 12952 0.00057387 Symetry 72/72 17184 0.00059295 Distributivity 504/504 210952 0.01117182 ########################################################################### Snappy  is really fast and respects, as do the others, the four conditions. It is interesting to see that the first property will never be satisfied by the NCD. This happens because in practice it is impossible to obtain those conditions even if we have close results (which is why the algorithm works :). It is possible to execute the similarity library in Elsim to use independently the Kolmogorov complexity and the NCD: ########################################### In : from elsim.similarity import similarity In : s = similarity.SIMILARITY("./elsim/similarity/libsimilarity/libsimilarity.so") // change the type of compressor (bzip2) In : s.set_compress_type( similarity.BZ2_COMPRESS ) // Get the kolmogorov complexity (by using the compressor, so this function // returns the length of the compression In : s.kolmogorov("W00T W00T PHRACK") Out: (52L, 0) // Get the similarity distance between two strings In : s.ncd("W00T W00T PHRACK", "W00T W00T PHRACK") Out: (0.057692307978868484, 0) In : s.ncd("W00T W00T PHRACK", "W00T W00T PHRACK STAFF") Out: (0.17543859779834747, 0) In : s.ncd("W00T W00T PHRACK", "HELLO WORLD") Out: (0.23076923191547394, 0) // As you can see : // - the elements of the first comparison are closer // than the elements of the second comparison // - the elements of the second comparison are closer // than the elements of the third comparison // - the result of the first comparison is not 0, that is why // we don't respect the first property but practically it works // because we are not far from 0 // change the type of compressor (Snappy) In : s.set_compress_type( similarity.SNAPPY_COMPRESS ) In : s.ncd("W00T W00T PHRACK", "W00T W00T PHRACK") Out: (0.6666666865348816, 0) In : s.ncd("W00T W00T PHRACK", "W00T W00T PHRACK STAFF") Out: (0.6818181872367859, 0) In : s.ncd("W00T W00T PHRACK", "HELLO WORLD") Out: (0.7777777910232544, 0) // As you can see, Snappy is very bad with such kind of strings, even if // the algorithm respects the dissimilarities between the comparison. // If we test this compressor with longer strings, and strings of // signatures (3.1), we have better results: In : s.ncd("B[I]B[RF1]B[F0S]B[IF1]B[]B[]B[S]B[SS]B[RF0]B[]B[SP0I]"\ "B[GP1]", "B[I]B[RF1]B[F0S]B[IF1]B[]B[]B[S]B[SS]B[RF0]B[]B[SP0I]B[GP1]") Out: (0.0784313753247261, 0) In : s.ncd("B[I]B[RF1]B[F0S]B[IF1]B[]B[]B[S]B[SS]B[RF0]B[]B[SP0I]"\ "B[GP1]", "B[I]B[RF1]B[F0S]B[IF1]B[]B[]B[S]B[SS]B[RF0]B[]B[SP0I]") Out: (0.11764705926179886, 0) In : s.ncd("B[I]B[RF1]B[F0S]B[IF1]B[]B[]B[S]B[SS]B[RF0]B[]B[SP0I]"\ "B[GP1]", "B[G]B[SGIGF0]B[RP1G]B[SP1I]B[SG]B[SSGP0]B[F1]B[P0SSGR]B[F1]"\ "B[SSSI]B[RF1P0R]B[GSP0RP0P0]B[GI]B[P1]B[I]B[GP1S]") Out: (0.9270833134651184, 0) ########################################### Snappy maybe the fastest algorithm but its rate compression is the worst. However, it is not of particular importance. Why? Because it is not a problem if the properties are respected. Moreover if you want an end value which respects more the idea of similarities, you can still switch to some other compressor, such as ZLIB, LZMA or BZ2. The first thing to do is to describe our "basic" element which will be used for a comparison. An element is composed of: - a string - a hash Oh wait, that's all? Yes, we need to compare strings and not other things. But the strings themselves will highly depend of your similarity problem. For example, if your problem is to compare two binaries, it's a bad idea to compare the listings corresponding to a specific function. You need to find the best way to transform your data into suitable strings, and it's probably the most difficult part. Of course it is not our job in this article:) It is not easy to transform your data to a string and it will be specific to each problem. For example, if your data is a chemical molecule you need probably to use SMILES to convert the structure to an ASCII string . Remember that you can't compare elements that easily, you really need a transformation because the Kolmogorov complexity is not magical. Using it requires the normalization of your data. Finally, the hash is only used to quickly remove the identical elements. The algorithm is the following one: - input: A:set(), B:set() where A and B are sets of elements - output: I:set(), S:set(), N:set(), D:set(), Sk:set() where I: identical elements, S: similar elements, N: new elements, D: deleted elements, Sk: skipped elements - Sk: Skipped elements by using a "filtering" function (helpful if we wish to skip some elements from a set (small size, known element from a library, etc.) - Identify internal identical elements in each set - I: Identify "identical" elements by the intersection of A and B - Get all others elements by removing identical elements - Perform the "NCD" between each element of A and B - S: "Sort" all similarities elements by using a threshold - N,D: Get all new/deleted elements if they are not present in one of the previous sets The following diagram describes this algorithm: |--A--| |--B--| | A1 | | B1 | | A2 | | B2 | | A3 | | B3 | |--An-| |--Bn-| | |---------| | |- --->|FILTERING|<-----| |---------| | | | |--------->|Sk| | | |---------| |----->|IDENTICAL|------>|I| |---------| | | | |---|---use-->|Kolmogorov| |---->|NCD| |---| | | | | |---------|-->|Threshold| |-------->| SORTING | |---------| | | /|\ / | \ / | \ / | \ / | \ / | \ |N|<------------/ | \-------->|D| | |---->|S| Moreover we can calculate a similarity "score" using the number of identical elements and the value of the similar elements. Here is a simple example showing you how it is possible to use the algorithm (elsim_text.py). In this case, it's used to compare two plain text files. We modified the COPYING.LESSER text by changing the order of few paragraphs and removing (or adding) words: ########################################### ds@t0t0:~/elsim$ ./tests/example_text_sim.py -i examples/text/COPYING.LESSER examples/text/COPYING.LESSER.MODIF_REORDER Elements: IDENTICAL: 106 SIMILAR: 2 NEW: 0 DELETED: 0 SKIPPED: 2 --> sentences: 99.783060% of similarities ########################################### As you can see, with a few modifications the two files are maximally similar even if the elements are not at the same place. And if you add some debugging information, you can see the two modified sentences: ########################################### [...] SIMILAR sentences: 138 'This version of the GNU Lesser General Public License incorporates the terms and conditions of version 3 of the GNU General Public License' --> 131 'This version of the GNU General Public License incorporates the terms and conditions of version 3 of the GNU General Public License' 0.105263158679 71 'and the "GNU GPL" refers to version 3 of the GNU General Public License' --> 76 'and the "GNU GPL HOOK" refers to version 3 of the GNU General Public License' 0.129032254219 [...] ########################################### ----[ 2.2 In a set of elements The previous algorithm is interesting in order to compare two elements, but if you have more or if you wish to search for a specific signature in a set of elements, it can be very long. That's why we need to use a clustering algorithm  to accelerate it. So, an element will be defined by: - a string (a signature), - a set of float values. The float values are classically features vectors that we will use the set of floats to perform the clustering (and you can use specific weights if you think that some elements of the set are more important than others). For example if you consider that the first float value if more important than the second one for clustering you can add a higher weight. In order to have more complex searches (i.e. if you wish to match multiple elements at the same time or only a specific element and not another, etc.) we will use a signature which will be composed of several elements and a Boolean formula whose purpose is to check if a signature matches. The algorithm is: - Load signatures from the database and elements - Execute a classical clustering  algorithm (kmeans  for example) to reduce the number of comparisons by using the set of float values - For each cluster, compare the loaded from the database signatures with the elements - If the NCD value is below the threshold and if the Boolean formula is true then we have found a valid signature (so we have a valid match!) |---SIGN---| |---ELEM---| | X1 | | E1 | | X2 | | E2 | | X3 | | E3 | |----Xn----| |----En----| | |----------| | |------->|CLUSTERING|<-------| |----------| | | | | | |->Cn | | | |->Cn-1 | | | | __C1__ |->| X1 | |---|---------->|Kolmogorov| | E1 |------>|NCD| | .. | ^ |---| | | | | | | | |---------->|Threshold| | | | | | | | | | / \ | / \ | / \ | F T | / \ |----------------/ | | | | |--------| | | BF | | |--------| | | | | | / \ | / \ | F T |------------------------/ \ | |-----| | OK | |-----| Simple, no ? :) Here is an example (example_sign.py) which shows you how to load signatures and elements, and check if a signature is present. In the following example, we have two signatures composed of elements and a set of "external" data to test. In the dataset, we have a corresponding signature and a false positive: ########################################### SIGNS = [ [ "Sign1", "a", [ [ 4.4915299415588379, 4.9674844741821289, 4.9468302726745605, 0.0 ], "HELLO WORLDDDDDDDDDDDDDDDDDDDDDDD" ] ], [ "Sign2", "a && b", [ [ 2.0, 3.0, 4.0, 5.0 ], "OOOPS !!!!!!!!" ], [ [ 2.0, 3.0, 4.0, 8.0], "OOOOOOOOPPPPPS !!!" ] ], ] ELEMS = [ [ [ 4.4915299415588379, 4.9674844741821289, 4.9468302726745605, 0.0 ], "HELLO WORLDDDDDDDDDDDDDDDDDDDDDDD" ], [ [ 4.4915299415588379, 4.9674844741821289, 4.9468302726745605, 1.0 ], "FALSE POSITIVE" ], [ [ 2.0, 3.0, 4.0, 5.0 ], "HELLO WORLDDDDDDDDDDDDDDDDDDDDDDD" ], [ [ 2.0, 3.0, 4.0, 5.0 ], "HELLO WORLDDDDDDDDDDDDDDDDDDDDDDD" ], [ [ 2.0, 3.0, 4.0, 5.0 ], "HELLO WORLDDDDDDDDDDDDDDDDDDDDDDD" ], [ [ 2.0, 3.0, 4.0, 5.0 ], "HELLO WORLDDDDDDDDDDDDDDDDDDDDDDD" ], ] ########################################### Each signature is composed of either one or several elements and a Boolean formula ("a" is the first element, "b" is the second element, etc.). By running the example, we can see that one signature is detected. It is displayed along with several statistics (such as the number of clusters, the number of comparisons (1) and the number of comparisons without (18) this algorithm). ########################################### d@t0t0:~/elsim/elsim/elsign$./example_sign.py ['Sign1', [0, 1, 0.1875]] [SIGN:3 CLUSTERS:3 CMP_CLUSTERS:2 ELEMENTS:6 CMP_ELEMENTS:1 -> 18 5.555556%] ########################################### If we remove the matching element, we can see that we can't detect a match of the signature anymore, even if we have close entropies (fake values in this case) with a signature (but the string is not the same): ########################################### d@t0t0:~/elsim/elsim/elsign$ ./example_sign.py [None] [SIGN:3 CLUSTERS:2 CMP_CLUSTERS:2 ELEMENTS:5 CMP_ELEMENTS:3 -> 15 20.000000%] ########################################### --[ 3 - Real World: Android Now we can apply our algorithms to a real world problem domain. We have chosen that of Android applications and malware identification. One of the main problems with Android Apps is the plagiarism due to the facilities to modify and spread an application. ----[ 3.1 Similarities between two applications To use our generic algorithm, we must first define what are the "string" and the "hash" properties of an element. So, what is an element in the case of an Android application? We define it as a method or a class. The "string" is the signature of a method and the "hash" is the sequence of instructions. Our signature is based on the grammar described by Silvio Cesare . This grammar is very simple: ######################################################################### Procedure ::= StatementList StatementList ::= Statement | Statement StatementList Statement ::= BasicBlock | Return | Goto | If | Field | Package | String Return ::= 'R' Goto ::= 'G' If ::= 'I' BasicBlock ::= 'B' Field ::= 'F'0 | 'F'1 Package ::= 'P' PackageNew | 'P' PackageCall PackageNew ::= '0' PackageCall ::= '1' PackageName ::= Epsilon | Id String ::= 'S' Number | 'S' Id Number ::= \d+ Id ::= [a-zA-Z]\w+ ######################################################################### For example if we have the following code: mov X, 4 mov Z, 5 add X, Z goto +50 add X, Z goto -100 Then the signature is: B[G]B[G] We do not take into account the different instructions but rather the information about the structure of the method. With an Android method, this gives a more complex signature: Code: [...] call [ meth@ 22 Ljava/lang/String; valueOf ['(I)', 'Ljava/lang/String;'] ] goto 50 Signature: B[P1{Ljava/lang/String; valueOf (I)Ljava/lang/String;}G] We only use the control flow graph (CFG) of the methods along with specific instructions of the CFG such as "if*" or "goto". All the instructions like sparse/packed switch  are translated to "goto" instructions without details. We can add information about the packages, and especially about the Android/Java packages. Indeed, it's an important information to include in the signature (e.g.: you must use the sendTextMessage API to send an SMS). In the signature we can also add if a method of a package is called, or if there is the creation of an object, or even if a field is read or written. Of course, it's possible to modify this kind of signature if you want to take into account each instruction of the method. However in our case (and after experimental results) it seems useless since we don't depend on the "nature" of each instruction, but only on higher level information. We can extend this concept by using "predefined" signatures to help us: - 0: information about packages (called/created) and fields, no specific information about string - 1: 0 + but with the size of strings, - 2: 0 + filtering android packages names, - 3: 0 + filtering Java packages names, - 4: 0 + filtering Android/Java packages. If we have different types of signatures, we are then able to change dynamically the signature in case the global structure of a function or the Android packages in the structure are more interesting to us. For example, if we disassemble a particular method using Androguard  or smali/baksmali , we obtain different signatures: ######################################################################### d@t0t0:~/androguard$./androlyze.py -s Androlyze version 1.0 In : a, d, dx = AnalyzeAPK("./examples/android/TestsAndroguard/bin/TestsAndroguard.apk") In : d.CLASS_Ltests_androguard_TestIfs.METHOD_testCFG.pretty_show() METHOD access_flags=public (Ltests/androguard/TestIfs; testCFG,()V) local registers: v0...v7 return:void testCFG-BB@0x0 : 0(0) const/4 v0 , [ #+ 1 ] // {1} 1(2) const/4 v1 , [ #+ 1 ] // {1} 2(4) const/4 v2 , [ #+ 1 ] // {1} 3(6) const/4 v3 , [ #+ 1 ] // {1} [ testCFG-BB@0x8 ] testCFG-BB@0x8 : 4(8) iget-boolean v4 , v7 , [ field@ 14 Ltests/androguard/TestIfs; Z P ] 5(c) if-eqz v4 , [ + 77 ] [ testCFG-BB@0x10 testCFG-BB@0xa6 ] testCFG-BB@0x10 : 6(10) move v1 , v0 7(12) iget-boolean v4 , v7 , [ field@ 15 Ltests/androguard/TestIfs; Z Q ] 8(16) if-eqz v4 , [ + 70 ] [ testCFG-BB@0x1a testCFG-BB@0xa2 ] testCFG-BB@0x1a : 9(1a) const/4 v3 , [ #+ 2 ] // {2} [ testCFG-BB@0x1c ] testCFG-BB@0x1c : 10(1c) add-int/lit8 v2 , v2 , [ #+ 1 ] [ testCFG-BB@0x20 ] testCFG-BB@0x20 : 11(20) sget-object v4 , [ field@ 0 Ljava/lang/System; Ljava/io/PrintStream; out ] 12(24) new-instance v5 , [ type@ 25 Ljava/lang/StringBuilder; ] 13(28) invoke-static v0 , [ meth@ 22 Ljava/lang/String; valueOf ['(I)', 'Ljava/lang/String;'] ] 14(2e) move-result-object v6 15(30) invoke-direct v5 , v6 , [ meth@ 25 Ljava/lang/StringBuilder; ['(Ljava/lang/String;)', 'V'] ] 16(36) const-string v6 , [ string@ 5 ',' ] 17(3a) invoke-virtual v5 , v6 , [ meth@ 31 Ljava/lang/StringBuilder; append ['(Ljava/lang/String;)', 'Ljava/lang/StringBuilder;'] ] 18(40) move-result-object v5 19(42) invoke-virtual v5 , v1 , [ meth@ 28 Ljava/lang/StringBuilder; append ['(I)', 'Ljava/lang/StringBuilder;'] ] 20(48) move-result-object v5 21(4a) const-string v6 , [ string@ 5 ',' ] 22(4e) invoke-virtual v5 , v6 , [ meth@ 31 Ljava/lang/StringBuilder; append ['(Ljava/lang/String;)', 'Ljava/lang/StringBuilder;'] ] 23(54) move-result-object v5 24(56) invoke-virtual v5 , v2 , [ meth@ 28 Ljava/lang/StringBuilder; append ['(I)', 'Ljava/lang/StringBuilder;'] ] 25(5c) move-result-object v5 26(5e) const-string v6 , [ string@ 5 ',' ] 27(62) invoke-virtual v5 , v6 , [ meth@ 31 Ljava/lang/StringBuilder; append ['(Ljava/lang/String;)', 'Ljava/lang/StringBuilder;'] ] 28(68) move-result-object v5 29(6a) invoke-virtual v5 , v3 , [ meth@ 28 Ljava/lang/StringBuilder; append ['(I)', 'Ljava/lang/StringBuilder;'] ] 30(70) move-result-object v5 31(72) invoke-virtual v5 , [ meth@ 32 Ljava/lang/StringBuilder; toString ['()', 'Ljava/lang/String;'] ] 32(78) move-result-object v5 33(7a) invoke-virtual v4 , v5 , [ meth@ 8 Ljava/io/PrintStream; println ['(Ljava/lang/String;)', 'V'] ] [ testCFG-BB@0x80 ] testCFG-BB@0x80 : 34(80) iget-boolean v4 , v7 , [ field@ 16 Ltests/androguard/TestIfs; Z R ] 35(84) if-eqz v4 , [ + 4 ] [ testCFG-BB@0x88 testCFG-BB@0x8c ] testCFG-BB@0x88 : 36(88) add-int/lit8 v3 , v3 , [ #+ 4 ] [ testCFG-BB@0x8c ] testCFG-BB@0x8c : 37(8c) iget-boolean v4 , v7 , [ field@ 17 Ltests/androguard/TestIfs; Z S ] 38(90) if-eqz v4 , [ + -8 ] [ testCFG-BB@0x94 testCFG-BB@0x80 ] testCFG-BB@0x94 : 39(94) add-int/lit8 v0 , v0 , [ #+ 6 ] 40(98) iget-boolean v4 , v7 , [ field@ 18 Ltests/androguard/TestIfs; Z T ] 41(9c) if-eqz v4 , [ + -74 ] [ testCFG-BB@0xa0 testCFG-BB@0x8 ] testCFG-BB@0xa0 : 42(a0) return-void testCFG-BB@0xa2 : 43(a2) const/4 v3 , [ #+ 3 ] // {3} 44(a4) goto [ + -68 ] [ testCFG-BB@0x1c ] testCFG-BB@0xa6 : 45(a6) add-int/lit8 v2 , v2 , [ #+ 2 ] 46(aa) goto [ + -69 ] [ testCFG-BB@0x20 ] ######################################################################### By using the first kind of predefined signature, we can see each basic block with some information. By filtering Java packages we have more information about the behavior of the method: ######################################################################### In : dx.get_method_signature(d.CLASS_Ltests_androguard_TestIfs. METHOD_testCFG, predef_sign = analysis.SIGNATURE_L0_0).get_string() Out: 'B[]B[I]B[I]B[]B[]B[P0P1P1P1P1P1P1P1P1P1P1]B[I]B[]B[I]B[I]B[R] B[G]B[G]' In : dx.get_method_signature(d.CLASS_Ltests_androguard_TestIfs. METHOD_testCFG, predef_sign = analysis.SIGNATURE_L0_3).get_string() Out: 'B[]B[I]B[I]B[]B[]B[P0{Ljava/lang/StringBuilder;}P1 {Ljava/lang/String;valueOf(I)Ljava/lang/String;} P1{Ljava/lang/StringBuilder;(Ljava/lang/String;)V} P1{Ljava/lang/StringBuilder;append(Ljava/lang/String;) Ljava/lang/StringBuilder;} P1{Ljava/lang/StringBuilder;append(I)Ljava/lang/StringBuilder;} P1{Ljava/lang/StringBuilder;append(Ljava/lang/String;) Ljava/lang/StringBuilder;} P1{Ljava/lang/StringBuilder;append(I)Ljava/lang/StringBuilder;} P1{Ljava/lang/StringBuilder;append(Ljava/lang/String;) Ljava/lang/StringBuilder;} P1{Ljava/lang/StringBuilder;append(I)Ljava/lang/StringBuilder;} P1{Ljava/lang/StringBuilder;toString()Ljava/lang/String;} P1{Ljava/io/PrintStream;println(Ljava/lang/String;)V}] B[I]B[]B[I]B[I]B[R]B[G]B[G]' ######################################################################### With SIGNATURE_L0_0 being 0 and SIGNATURE_L0_3 being 3. We can test our signature with a real malware like Foncy : ######################################################################### In : a, d, dx = AnalyzeAPK("./apks/malwares/foncy/6be2988a916cb620c71ff3d8d4dac5db2881c6\ 75dd34a4bb7b238b5899b48600") ######################################################################### In this case, we are more interested in signatures embedding Android packages, Java packages or both: ######################################################################### In : dx.get_method_signature(d.CLASS_Lorg_eapp_MagicSMSActivity. METHOD_onCreate, predef_sign = analysis.SIGNATURE_L0_2).get_string() Out: 'B[P1{Landroid/app/Activity;onCreate(Landroid/os/Bundle;)V}P0 P1{Landroid/os/Environment;getExternalStorageDirectory()Ljava/io/File;}P1 P1P1P1P1P0P0P1P1P1P1P1P1I]B[R]B[P1] B[P1{Landroid/telephony/SmsManager;getDefault() Landroid/telephony/SmsManager;} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V} P1{Landroid/telephony/SmsManager;sendTextMessage(Ljava/lang/String; Ljava/lang/String; Ljava/lang/String; Landroid/app/PendingIntent; Landroid/app/PendingIntent;)V}P2 P1{Landroid/widget/Toast;makeText(Landroid/content/Context; Ljava/lang/CharSequence; I)Landroid/widget/Toast;} P1{Landroid/widget/Toast;show()V}G]B[G]' In : dx.get_method_signature(d.CLASS_Lorg_eapp_MagicSMSActivity. METHOD_onCreate, predef_sign = analysis.SIGNATURE_L0_3).get_string() Out: 'B[P1P0{Ljava/lang/StringBuilder;}P1 P1{Ljava/io/File;getAbsolutePath()Ljava/lang/String;} P1{Ljava/lang/String;valueOf(Ljava/lang/Object;)Ljava/lang/String;} P1{Ljava/lang/StringBuilder;(Ljava/lang/String;)V} P1{Ljava/lang/StringBuilder;append(Ljava/lang/String;) Ljava/lang/StringBuilder;} P1{Ljava/lang/StringBuilder;toString()Ljava/lang/String;} P0{Ljava/io/File;} P0{Ljava/lang/StringBuilder;} P1{Ljava/lang/String;valueOf(Ljava/lang/Object;)Ljava/lang/String;} P1{Ljava/lang/StringBuilder;(Ljava/lang/String;)V} P1{Ljava/lang/StringBuilder;append(Ljava/lang/String;) Ljava/lang/StringBuilder;} P1{Ljava/lang/StringBuilder;toString()Ljava/lang/String;} P1{Ljava/io/File;(Ljava/lang/String;)V} P1{Ljava/io/File;exists()Z}I]B[R] B[P1{Ljava/io/File;createNewFile()Z}] B[P1P1P1P1P1P1P1P1P1P1P1P1P1P1P1P1P1P1P1P1P1P2P1P1G]B[G]' In : dx.get_method_signature(d.CLASS_Lorg_eapp_MagicSMSActivity. METHOD_onCreate, predef_sign = analysis.SIGNATURE_L0_4).get_string() Out: 'B[P1{Landroid/app/Activity;onCreate(Landroid/os/Bundle;)V} P0{Ljava/lang/StringBuilder;} P1{Landroid/os/Environment;getExternalStorageDirectory()Ljava/io/File;} P1{Ljava/io/File;getAbsolutePath()Ljava/lang/String;} P1{Ljava/lang/String;valueOf(Ljava/lang/Object;)Ljava/lang/String;} P1{Ljava/lang/StringBuilder;(Ljava/lang/String;)V} P1{Ljava/lang/StringBuilder;append(Ljava/lang/String;) [...] Landroid/app/PendingIntent;)V} [...] B[G]' ######################################################################### It's interesting to see that even if our basic blocks are in a different order, the Kolmogorov complexity is preserved and that we observe an important similarity (TestReorg function). If we reorganize each basic block in the signature we can see that the results are quite the same (so basically the NCD bypasses a basic CFG obfuscation): ######################################################################### d@t0t0:~/elsim$ ./tests/test_similarity.py * LZMA (0.031779661774635315, 0) (0.031779661774635315, 0) (0.04237288236618042, 0) (0.040169134736061096, 0) (0.03983228653669357, 0) (0.03991596773266792, 0) (0.042016807943582535, 0) (0.039256200194358826, 0) (0.04356846585869789, 0) (0.03933747485280037, 0) (0.03719008341431618, 0) (0.043478261679410934, 0) (0.043478261679410934, 0) (0.04025423899292946, 0) (0.04411764815449715, 0) (0.041580040007829666, 0) (0.04149377718567848, 0) (0.03563941270112991, 0) (0.03966597095131874, 0) (0.03563941270112991, 0) (0.04184100404381752, 0) (0.04393305256962776, 0) (0.03974895551800728, 0) (0.03983228653669357, 0) (0.041753653436899185, 0) [....] ######################################################################### The "hash" is the sequence of instructions in each method, and for each instruction we remove the information depending on the compilation (registers, etc.). Having defined the "string" and the "hash" properties in the specific context of Android Apps, we can now test the algorithm on various samples. We use a tool called "androsim.py" which is a simple script based on "Elsim". This tool detects and reports: - the identical methods; - the similar methods; - the deleted methods; - the new methods; - the skipped methods. Moreover, a similarity score (between 0.0 to 100.0) is calculated upon the values of the identical methods (1.0) and the similar methods (in this particular case, we calculate the final values using the BZ2 compressor due to the fact that the return value is more "interesting" for the score). It is more interesting because you will have an understandable value related to the similarity. For the first test we use the "opfake" malware . If we take two samples from the same family, an important value of similarity is revealed: ######################################################################### d@t0t0:~/androguard$./androsim.py -i apks/malwares/opfake/\ b79106465173490e07512aa6a182b5da558ad2d4f6fae038101796b534628311 apks/malwares/opfake/\ b906279e8c79a12e5a10feafe5db850024dd75e955e9c2f9f82bbca10e0585a6 Elements: IDENTICAL: 34 SIMILAR: 5 NEW: 0 DELETED: 0 SKIPPED: 0 --> methods: 99.100500% of similarities ######################################################################### These two samples have similar methods and it's possible to have more information by specifying the "-d" option: ######################################################################### SIMILAR methods: Lcom/reg/MainRegActivity; displayFakeProgress ()V 61 --> Lcom/reg/MainRegActivity; displayFakeProgress ()V 61 0.0909090936184 Lcom/reg/MainRegActivity; getNextButton ()Landroid/widget/Button; 40 --> Lcom/reg/MainRegActivity; getNextButton ()Landroid/widget/Button; 40 0.125 Lcom/reg/MainRegActivity; showLinkForm ()V 111 --> Lcom/reg/MainRegActivity; showLinkForm ()V 111 0.183673471212 Lcom/reg/MainRegActivity; showRules ()V 132 --> Lcom/reg/MainRegActivity; showRules ()V 132 0.0731707289815 Lcom/reg/MainRegActivity; setMainScreen ()V 147 --> Lcom/reg/MainRegActivity; setMainScreen ()V 147 0.319148927927 IDENTICAL methods: Lcom/reg/MainRegActivity; PushMsg (Ljava/lang/String; Ljava/lang/String;)V 76 --> Lcom/reg/MainRegActivity; PushMsg (Ljava/lang/String; Ljava/lang/String;)V 76 Lcom/reg/SmsReceiver; setListener (Lcom/reg/SMSAction;)V 3 --> Lcom/reg/SmsReceiver; setListener (Lcom/reg/SMSAction;)V 3 Lcom/reg/MainRegActivity; loadString (I)Ljava/lang/String; 52 --> Lcom/reg/MainRegActivity; loadString (I) Ljava/lang/String; 52 Lcom/reg/MainRegActivity; access$600 ()Ljava/lang/String; 3 --> Lcom/reg/MainRegActivity; access$600 ()Ljava/lang/String; 3 Lcom/reg/ParseXml; getXMLTags (Ljava/lang/String; Ljava/lang/String;)Ljava/util/Vector; 82 --> Lcom/reg/ParseXml; getXMLTags (Ljava/lang/String; Ljava/lang/String;)Ljava/util/Vector; 82 Lcom/reg/ParseXml; getXMLExtra (Ljava/lang/String; Ljava/lang/String;)Ljava/lang/String; 52 --> Lcom/reg/ParseXml; getXMLExtra (Ljava/lang/String; Ljava/lang/String;)Ljava/lang/String; 52 Lcom/reg/MainRegActivity; SaveSuccess ()V 23 --> Lcom/reg/MainRegActivity; SaveSuccess ()V 23 Lcom/reg/SmsReceiver; onReceive (Landroid/content/Context; Landroid/content/Intent;)V 59 --> Lcom/reg/SmsReceiver; onReceive (Landroid/content/Context; Landroid/content/Intent;)V 59 Lcom/reg/ParseXml; getXMLIntElement (Ljava/lang/String; Ljava/lang/String;)I 55 --> Lcom/reg/ParseXml; getXMLIntElement (Ljava/lang/String; Ljava/lang/String;)I 55 Lcom/reg/MainRegActivity; getCountry ()Ljava/lang/String; 13 --> Lcom/reg/MainRegActivity; getCountry ()Ljava/lang/String; 13 Lcom/reg/MainRegActivity$5; onReceive (Landroid/content/Context; Landroid/content/Intent;)V 35 --> Lcom/reg/MainRegActivity$5; onReceive (Landroid/content/Context; Landroid/content/Intent;)V 35 Lcom/reg/MainRegActivity$1; (Lcom/reg/MainRegActivity;)V 6 --> Lcom/reg/MainRegActivity$1; (Lcom/reg/MainRegActivity;)V 6 Lcom/reg/MainRegActivity$S_itm; (Lcom/reg/MainRegActivity;)V 21 --> Lcom/reg/MainRegActivity$S_itm; (Lcom/reg/MainRegActivity;)V 21 [...] NEW methods: DELETED methods: SKIPPED methods: ######################################################################### Basically we are able to determine if two samples are from the same malware family. If they are, the analyst can start his analysis from the similar methods. In the next part we will see how we can see the differences (what instructions have been modified) between two similar methods. If we test the tool by using two different samples (like opfake and foncy) we observe the following: ######################################################################### d@t0t0:~/androguard$ ./androsim.py -i apks/malwares/opfake/\ b79106465173490e07512aa6a182b5da558ad2d4f6fae038101796b534628311 apks/malwares/foncy/\ 01f6f6379543f4aaa0d6b8dcd682f4e2b106527584b3645eb674f1646faccad5 Elements: IDENTICAL: 1 SIMILAR: 0 NEW: 2 DELETED: 38 SKIPPED: 0 --> methods: 33.333333% of similarities ######################################################################### We see a strange similarity score due to the fact that all methods, including those of small size, have been compared. We can skip the specific case of methods having a small size using the "-s" option (to filter according to the size of the method in bytes): ######################################################################### d@t0t0:~/androguard$./androsim.py -i apks/malwares/opfake/\ b79106465173490e07512aa6a182b5da558ad2d4f6fae038101796b534628311 apks/malwares/foncy/\ 01f6f6379543f4aaa0d6b8dcd682f4e2b106527584b3645eb674f1646faccad5 -s 10 Elements: IDENTICAL: 0 SIMILAR: 0 NEW: 2 DELETED: 29 SKIPPED: 33 --> methods: 0.000000% of similarities ######################################################################### We can do a lot of things with this kind of tool such as: - detecting plagiarism between two android applications - checking if an application is correctly protected with an obfuscator - extracting easily injected codes (if you know the original application) There are many other interesting "ways" to use this tool such as discovering if malware samples have been written by the same author, or if some pieces of code have been reused. Analyzing the "faketoken"  sample and the "opfake.d" sample we have observed an interesting result. The first sample "faketoken" is detected by 19/43 antivirus products on VirusTotal . The second sample "opfake.d" is detected by 16/41 antivirus products on VirusTotal . All of these antivirus products are using different names with the exception of DrWeb. Now if we run our tool we observe the following output: ######################################################################### d@t0t0:~/androguard$ ./androsim.py -i apks/plagiarism/opfake/\ f7c36355c706fc9dd8954c096825e0613807e0da4bd7f3de97de0aec0be23b79 apks/plagiarism/opfake/\ 61da462a03d8651a6088958b438b44527973601e604e3ca18cb7aa0b3952d2ac Elements: IDENTICAL: 951 SIMILAR: 5 NEW: 34 DELETED: 23 SKIPPED: 0 --> methods: 96.516954% of similarities ######################################################################### We can skip specific libraries common to these samples such as "Lorg/simpleframework/xml" and methods of small sizes. This provides us with an even more interesting result: ######################################################################### d@t0t0:~/androguard./androsim.py -i apks/plagiarism/opfake/\ f7c36355c706fc9dd8954c096825e0613807e0da4bd7f3de97de0aec0be23b79 apks/plagiarism/opfake/\ 61da462a03d8651a6088958b438b44527973601e604e3ca18cb7aa0b3952d2ac -e "Lorg/simpleframework/" -s 100 -d Elements: IDENTICAL: 9 SIMILAR: 3 NEW: 14 DELETED: 11 SKIPPED: 5260 --> methods: 44.998713% of similarities SIMILAR methods: Ltoken/bot/MainApplication; loadStartSettings (Ljava/lang/String;)Ltoken/bot/StartSettings; 230 --> Lcom/load/wap/MainApplication; loadStartSettings (Ljava/lang/String;)Lcom/load/wap/StartSettings; 190 0.375 Ltoken/bot/MainService; threadOperationRun (I Ljava/lang/Object;)V 197 --> Lcom/load/wap/MainService; threadOperationRun (I Ljava/lang/Object;)V 122 0.319999992847 Ltoken/bot/ServerResponse; ()V 133 --> Lcom/load/wap/ServerResponse; ()V 125 0.214285716414 IDENTICAL methods: Ltoken/bot/Settings; isDeleteMessage (Ljava/lang/String; Ljava/lang/String;)Z 132 --> Lcom/load/wap/Settings; isDeleteMessage (Ljava/lang/String; Ljava/lang/String;)Z 132 Ltoken/bot/UpdateActivity; setMainScreen ()V 107 --> Lcom/load/wap/UpdateActivity; setMainScreen ()V 107 Ltoken/bot/MainApplication; sendGetRequest (Ljava/lang/String; Ljava/util/List;)V 132 --> Lcom/load/wap/MainApplication; sendGetRequest (Ljava/lang/String; Ljava/util/List;)V 132 Ltoken/bot/MainService; onStart (Landroid/content/Intent; I)V 106 --> Lcom/load/wap/MainService; onStart (Landroid/content/Intent; I)V 106 Ltoken/bot/MainApplication; sendPostRequest (Ljava/lang/String; Ljava/util/List;)V 197 --> Lcom/load/wap/MainApplication; sendPostRequest (Ljava/lang/String; Ljava/util/List;)V 197 Ltoken/bot/MainApplication; DownloadApk (Ljava/lang/String; Ljava/lang/String;)Z 106 --> Lcom/load/wap/MainApplication; DownloadApk (Ljava/lang/String; Ljava/lang/String;)Z 106 Ltoken/bot/Settings; isCatchMessage (Ljava/lang/String; Ljava/lang/String;)Ltoken/bot/CatchResult; 165 --> Lcom/load/wap/Settings; isCatchMessage (Ljava/lang/String; Ljava/lang/String;) Lcom/load/wap/CatchResult; 165 Ltoken/bot/MainApplication; getContacts (Landroid/content/Context;)Ljava/util/Vector; 230 --> Lcom/load/wap/MainApplication; getContacts (Landroid/content/Context;)Ljava/util/Vector; 230 Ltoken/bot/MainApplication; dateFromString (Ljava/lang/String;)Ljava/util/Date; 103 --> Lcom/load/wap/MainApplication; dateFromString (Ljava/lang/String;)Ljava/util/Date; 103 ######################################################################### As we can see, the names of the methods are "exactly" the same, and the signatures (the bytecodes with a high probability) are the same. It can be really interesting to detect if your software has been ripped off by someone. ----[ 3.2 Differences between two applications Up to this point, we have a tool which is able to recognize similar methods, but we would like more information about the differences between each method. For that we will apply the same algorithm but we will change the "granularity" and focus on basic blocks in order to extract differences. However, in this specific case, we will not use our classical signature for each basic block but rather a simple "string" which represents the sequence of instructions. So, finally, as in the previous algorithm, we will have: - identical basic blocs - similar basic blocs - new basic blocs - deleted basic blocs With the list of similar basic blocks, we can apply a standard "diff" algorithm between each similar basic blocks to know which instructions have been added or removed. The Longuest Common Subsequence (LCS) algorithm  can then be used to obtain all differences. In order to apply the LCS algorithm, we will map each unique instruction to a simple string: ADD 3 -> "\00" ADD 1 -> "\01" MOV 3 -> "\02" ADD 3 -> "\00" If we have two basic blocks, we must translate each basic block into a final string: ADD 3 ADD 1 SUB 2 IGET => "\x00\x01\x02\x03\x00\x04" ADD 3 GOTO ADD 3 ADD 3 SUB 2 IGET => "\x00\x00\x02\x03\x05\x04" MUL 4 GOTO The application of the LCS algorithm between these two strings reveals the instructions that have been added or removed: ######################################################################### In : from elsim_dalvik.py import LCS In : a = "\x00\x01\x02\x03\x00\x04" In : b = "\x00\x00\x02\x03\x05\x04" In : z = LCS(a, b) In : from elsim_dalvik import getDiff In : l_a = [] In : l_r = [] In : getDiff(z, a, b, len(a), len(b), l_a, l_r) In : l_a Out: [(1, '\x00'), (4, '\x05')] // "ADD 3" and "MUL 4" have been added in the second basic bloc In : l_r Out: [(1, '\x01'), (4, '\x00')] // ""ADD 1" and "ADD 3" have been remove in the first basic bloc ######################################################################### Although it's also possible to use a better algorithm such as the Needleman algorithm  (used in biology for "sequence alignment" , or in the comparison of network traces ), the tests performed have demonstrated that the LCS algorithm was sufficient. Now, we have a new tool called "androdiff.py" which can be used to extract and observe differences between two Android applications. We have tested it against two versions of the Skype application to analyze the patch of a security vulnerability  (mainly due to incorrect use of file permissions): ######################################################################### d@t0t0:~/androguard ./androsim.py -i elsim/examples/android/com.skype.raider_1.0.0.831.apk elsim/examples/android/com.skype.raider_1.0.0.983.apk -c BZ2 Elements: IDENTICAL: 2059 SIMILAR: 167 NEW: 27 DELETED: 0 SKIPPED: 0 --> methods: 98.192539% of similarities ######################################################################### We have several methods to analyze, but only a few new methods are present, and two of them are particularly interesting: ######################################################################### Lcom/skype/ipc/SkypeKitRunner; chmod (Ljava/io/File; Ljava/lang/String;)Z 61 Lcom/skype/ipc/SkypeKitRunner; fixPermissions ([Ljava/io/File;)V 47 ######################################################################### So we can now search in the similar methods where these new methods are called: ######################################################################### d@t0t0:~/androguard$./androdiff.py -i elsim/examples/android/com.skype.raider_1.0.0.831.apk elsim/examples/android/com.skype.raider_1.0.0.983.apk -d [...] [ ('Lcom/skype/ipc/SkypeKitRunner;', 'run', '()V') ] <-> [ ('Lcom/skype/ipc/SkypeKitRunner;', 'run', '()V') ] run-BB@0xae run-BB@0xae Added Elements(2) 0xba 3 invoke-virtual v8 , [ meth@ 5897 Ljava/security/MessageDigest; reset ['()', 'V'] ] 0xc0 4 sget-object v9 , [ field@ 1299 Lcom/skype/ipc/SkypeKitRunner; [B MAITSEAINE ] Deleted Elements(0) run-BB@0x320 run-BB@0x316 Added Elements(1) 0x332 5 const/4 v8 , [ #+ 0 ] // {0} Deleted Elements(1) 0x328 5 const/4 v8 , [ #+ 3 ] // {3} run-BB@0x352 run-BB@0x348 Added Elements(1) 0x364 4 const-string v5 , [ string@ 2921 'chmod 750 ' ] Deleted Elements(1) 0x35a 4 const-string v5 , [ string@ 2904 'chmod 777 ' ] run-BB@0x52c run-BB@0x522 Added Elements(10) 0x59e 29 invoke-virtual v4 , [ meth@ 109 Landroid/content/Context; getFilesDir ['()', 'Ljava/io/File;'] ] 0x5a4 30 move-result-object v4 0x5a6 31 invoke-virtual v4 , [ meth@ 5719 Ljava/io/File; getAbsolutePath ['()', 'Ljava/lang/String;'] ] 0x5ac 32 move-result-object v4 0x5be 37 move-object/from16 v0 , v19 0x5c2 38 iget-object v0 , v0 , [ field@ 1314 Lcom/skype/ipc/SkypeKitRunner; Landroid/content/Context; mContext ] 0x5c6 39 move-object v4 , v0 0x5d8 44 move-object/from16 v0 , v19 0x5dc 45 move-object v1 , v4 0x5de 46 invoke-direct v0 , v1 , [ meth@ 1923 Lcom/skype/ipc/SkypeKitRunner; fixPermissions ['([Ljava/io/File;)', 'V'] ] Deleted Elements(0) [...] ######################################################################### As you can see, some constants are changed (3 to 0, 777 to 750) to patch an incorrect use of file permissions (you need to take the original CFG to view the details (maybe in a new version we will see the results in one CFG)). A new method is called to fix the existing permissions of the files. ----[ 3.3 Looking for a signature in applications Now, if you wish to detect if a specific method (or a class) is present in another application, you need to check all methods of this application with your method. Moreover, if we have a database of signatures, we must check if each signature is present in our application. For example, if your database is composed of 1000 signatures, and our application contains 1000 methods we will need to perform: - 1000 * 1000 -> 1.000.000 of comparisons to know the result That's why we need another solution and we will use the second algorithm (2.2). In this algorithm we need a set of float values to perform the clustering. So, in this example, we will use different sources of entropies. We have already described the generic algorithm (2.2), so we only need to define our element in this implementation. An element (in fact a part of our signature) will be composed of: - a string which represents the method (or the class) (in fact it is a signature obtained by the grammar (3.1)) - a set of entropies (float values) The most important part is the set of entropies. We have used different sources of entropies to have better results. An Android application provides an important amount of information. One of them is the API that is used (the Android/Java API). Another one is the exceptions because they define very well a method. We can also use the entropy of the signature and the bytecode. Maybe we have redundancy by using these entropies (due to the fact that the entropy of the signature is composed of both the Android/Java packages and the exceptions), so we need to work more on this subject but this problem will not produce false positives. We will also define a simple JSON file that we will use to generate our signature in order to extract information like the entropies and to add it in a database. We take the "logastrod"  malware and we create the signature after the analysis of this malware in order to find where are the most interesting malicious parts: ######################################################################### d@t0t0:~/androguard$ cat signatures/logastrod.sign [ { "SAMPLE" : "apks/malwares/logastrod/ \ f18891b20623ad35713e7f44feade51a1fd16030af55056a45cefa3f5f38e983" }, { "BASE" : "AndroidOS", "NAME" : "Logastrod", "SIGNATURE" : [ { "TYPE" : "METHSIM", "CN" : "Lcom/pavel/newmodule/RuleActivity;", "MN" : "onCreate", "D" : "(Landroid/os/Bundle;)V" }, { "TYPE" : "METHSIM", "CN" : "Lcom/pavel/newmodule/LicenseActivity;", "MN" : "onCreate", "D" : "(Landroid/os/Bundle;)V" } ], "BF" : "a && b" } ] ######################################################################### The name of this signature is "Logastrod" and we need to recognize the two methods (the boolean formula) to make a positive match. By using the "androcsign.py" tool we can extract the entropies and signatures of the methods from the specified sample, and add it to our database: ######################################################################### d@t0t0:~/androguard$./androcsign.py -i signatures/logastrod.sign -d signatures/dbandroguard [{u'Logastrod': [[[0, 'Qlt[...]kdd', 4.809434597538392, 4.584117420715886, 4.538809415871831, 0.0]], u'a && b' ]}] ######################################################################### Now it is possible to use "androsign.py" to check a particular file or an entire directory by using a database of signatures. "f22affca4ea15e58d8b4d345e54a7910b03c37fa70941bbcf36659cb809f13d9" is a sample of this "logastrod" malware: ######################################################################### d@t0t0:~/androguard$ ./androsign.py -i apks/malwares/logastrod/f22affca4ea15e58d8b4d345e54a7910b03c37fa70941bbcf36 659cb809f13d9 -b signatures/dbandroguard -c signatures/dbconfig -v [SIGN:69 CLUSTERS:10 CMP_CLUSTERS:8 ELEMENTS:31 CMP_ELEMENTS:39 -> 2139 1.823282%] [[91, 92, 0.27931034564971924], [91, 93, 0.18803419172763824]] ----> Logastrod ######################################################################### As you can see, we have only done "39" comparisons thanks to the clustering. Without this method, "2139" comparisons would have been required for the same result. ######################################################################### d@t0t0:~/androguard./androsign.py -d apks/malwares/logastrod/ -b signatures/dbandroguard -c signatures/dbconfig f22affca4ea15e58d8b4d345e54a7910b03c37fa70941bbcf36659cb809f13d9 : ----> Logastrod a0a42b9f1d45a0e09a8da6d9ce8e74952340a538251d0e697cfe1b16e5ac6696 : ----> Logastrod 77943921c7d6bad5f2e45fa22df4c23d034021ae56f0b09ecac8efb97830e0de : ----> Logastrod fea4dd75dfc4bfe279faf0b7675c48166ecac57bc8e8436c277a6da20582892f : ----> Logastrod f18891b20623ad35713e7f44feade51a1fd16030af55056a45cefa3f5f38e983 : ----> Logastrod e45caa25f87531cff2ee2803374ac78de0757941dd1311e3411ce4cdf6d5d942 : ----> Logastrod ######################################################################### We can see that on VirusTotal all these samples are not detected identically by few AV products: ######################################################################### f22affca4ea15e58d8b4d345e54a7910b03c37fa70941bbcf36659cb809f13d9 : 22/43 antivirus a0a42b9f1d45a0e09a8da6d9ce8e74952340a538251d0e697cfe1b16e5ac6696 : 19/43 antivirus 77943921c7d6bad5f2e45fa22df4c23d034021ae56f0b09ecac8efb97830e0de : 22/43 antivirus fea4dd75dfc4bfe279faf0b7675c48166ecac57bc8e8436c277a6da20582892f : 21/43 antivirus f18891b20623ad35713e7f44feade51a1fd16030af55056a45cefa3f5f38e983 : 19/43 antivirus e45caa25f87531cff2ee2803374ac78de0757941dd1311e3411ce4cdf6d5d942 : 21/43 antivirus ######################################################################### We maintain an Open Source Database of Android Malware  where you can find analysis links and a few signatures for Android malware. The main difficulty is to create a signature because you must choose carefully which method/class you wish to add to the database in order to avoid as much as possible false positives. In other terms, don't add a method/class from a free/proprietary "API" or project in a malware database :) You can use this tool to check if your application has been stolen by someone else using a multiple file analysis. Imagine that you have created an uber open source && l33t algorithm and you wish to know if your algorithm has been ripped off and included in a proprietary software. Of course, it is possible to build databases of many "things", from a cryptographic functions database to a DNA database... --[ 4 - Conclusion The similarity is a difficult problem but it is possible to achieve an interesting result by using the NCD and the entropy with "normalized" data. So, at the end of this paper, you will find two tools. The first one is Androguard in the first stable 1.0 version. Androguard is a known framework in Python to manipulate, reverse engineer, and play with Android applications. The stable version we release with this paper brings a lot of new things (especially the stability of the similarities tools) and a few tips and tricks to reverse engineer Android Apps (such as dealing with non-ASCII names). In this framework, several tools are using the new open source software Elsim to search the similarities/dissimilarities in different sets of elements. We have described two kinds of "generic" algorithms. The first one can be used if you wish to find the similarities between two sets of elements. The second one can be used if you have a database of signatures and you need a quick engine to search the signatures in a set of elements. Finally we described a new algorithm of entropy ("Descriptional entropy") which can be used to classify and obtain more information from an element and two new algorithms which can help you to answer to a similarity "problem". But Elsim is not limited to Android applications, and the tool will be improved in the next months to support x86 and ARM binaries in order to have an open source software with such capabilities. Many thanks to the Phrack staff for the suggestions on how to improve this work. "Talk is cheap. Show me the code". Torvalds, Linus" --[ 5 - References  Androguard. http://code.google.com/p/androguard/  Silvio Cesare (2010). "Classification of malware using structured control flow".  MacQueen, J. B. (1967). "Some Methods for classification and Analysis of Multivariate Observations".  Android source code (dalvik). http://source.android.com/  Foncy Android Malware. http://code.google.com/p/androguard/wiki/DatabaseAndroidMalwares#foncy  Opfake Android Malware. http://code.google.com/p/androguard/wiki/DatabaseAndroidMalwares#opfake_\ (all)  Faketoken Android Malware. http://code.google.com/p/androguard/wiki/DatabaseAndroidMalwares#faketoken  https://www.virustotal.com/file/\ f7c36355c706fc9dd8954c096825e0613807e0da4bd7f3de97de0aec0be23b79/analysis/  https://www.virustotal.com/file/\ 61da462a03d8651a6088958b438b44527973601e604e3ca18cb7aa0b3952d2ac/analysis/  Needleman, Saul B and Wunsch, Christian D. (1970). "A general method applicable to the search for similarities in the amino acid sequence of two proteins"  L. Bergroth and H. Hakonen and T. Raita (2000). "A Survey of Longest Common Subsequence Algorithms".  Sequence Alignement. http://en.wikipedia.org/wiki/Sequence_alignment  Android Police. http://www.androidpolice.com/2011/04/14/\ exclusive-vulnerability-in-skype-for-android-is-exposing-your-name\ -phone-number-chat-logs-and-a-lot-more/.  Logastrod Android Malware. http://code.google.com/p/androguard/wiki/DatabaseAndroidMalwares#Logastrod  Opensource Database of Android Malware. http://code.google.com/p/androguard/wiki/DatabaseAndroidMalwares  Manuel Cebrian, Manuel Alfonseca and Alfonso Ortega. "Common Pitfalls Using Normalized Compression Distance: What to Watch Out for in a Compressor"  R. Cilibrasi and P. M. B. Vitanyi. "Clustering by compression"  Kolmogorov A. N (1965). "Three Approaches for Defining the Concept of Information Quantity"  Snappy compressor. http://code.google.com/p/snappy/  Cilibrasi, R. & Vitanyi, P. (2005). "Clustering by compression"  Dullien, T. & Rolles, R. (2005). "Graph-based comparison of executable objects"  M. Li and P. Vitanyi (1997). "An introduction to Kolmogorov Complexity and Its Applications"  D. Sankoff and J. Kruskal (1983, 1989). "Time warps, string edits and macromolecules"  J. Shallit, M.-W. Wang. "Automatic Complexity of Strings".  T. Sabin. "Comparing binaries with graph isomorphisms". http://razor.bindview.com/publish/papers/comparingbinaries.html  Wikipedia: http://en.wikipedia.org/wiki/Kolmogorov_complexity  Jesus Freke. http://code.google.com/p/smali/  http://en.wikipedia.org/wiki/Cluster_analysis  A. D. Danaksok and F. G. Gologlu, On Lempel-Ziv. "Complexity of Sequences"  Lempel, A., Ziv, J. "On the complexity of finite sequences"  S. Janson, S. Lonardi and W. Szpankowski. "On average sequence complexity"  J. Shallit. "On the maximum number of distinct factors in a binary string"  http://www.c-sharpcorner.com/uploadfile/acinonyx72/calculating\ -the-normalized-compression-distance-between-two-strings/  SMILES http://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system  Netzob http://www.netzob.org/ --[ 6 - Code begin 664 androguard-1.0.tar.gz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