24 Java Android Android (Dynamic Analysis) (Obfuscation) (Plagiarism) 1
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- ありあ ほうねん
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1 25 2 5
2 24 Java Android Android (Dynamic Analysis) (Obfuscation) (Plagiarism) 1
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4 1 [17, 21] [5] [10, 19] [9] [6] API [16] 2 Java C# [19] 2 Java 2 Android Android 3
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6 2 2.1 [1, 11] [7, 9, 12, 14, 18, 23] 5 CCFinder[9] CCFinder 4 Step 1. Step 2. 5
7 Step 3. Step [22] 1 Amida[20] Amida Java Virtual Machine Tool Interface(JVMTI) 1 Javassist 2 Java [22] 1 [13] Least Recently Used (LRU) Algorithm 1 [e 1, e 2,..., e last ] 4 c w m threshold e k k ID e k.caller ID e k.callee e k.callstack 4 1 JVMTI: 2 Javassist: 6
8 Algorithm 1 [22] procedure DetectPhases ( in [e 1, e 2,..., e last ]: list of method call event; in c, w, m: integer; in threshold: double; out P : set of integer); 1. LRUCache C new LRU cache(c) 2. integer[] update [] 3. double[] frequency [] 4. set of integer P ϕ 5. for integer t in [1... last] do 6. boolean b1 C.update(e t.caller, t) 7. boolean b2 C.update(e t.callee, t) 8. if b1 b2 then update[t] 1 else update[t] 0 9. double flags for integer k in [max(t w + 1, 1)... t] do 11. flags flags + update[k] 12. end for 13. f requency[t] f lags/w 14. if f requency[t] threshold then 15. integer min max(t m + 1, 1) 16. for integer k in [max(t m + 1, 1)... t] do 17. if e min.callstack e x.callstack then min k 18. end for 19. P P.add(min) 20. end if 21. end for 7
9 Algorithm 1 4 c Algorithm 1 1 LRU 1 1 w LRU frequency (Algorithm ) 1 (Algorithm 1 last) m (Algorithm ) 1 3 threshold LRU frequency (Algorithm 1 14 ) 0 1 LRU 3 Step 1. (Algorithm ) LRU C Step 2. (Algorithm ) t LRU C (frequency(t)) f requency(t) = tk=max(1,t w+1) update[k] w 8
10 update[k] k 1 0 frequency(t) t w threshold Step 3. (Algorithm ) m P = [t 1, t 2,..., t p ] 2 Java [19] [2, 19] ( ) API 1 (a) a b 1 (b) public static 9
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12 3 4 5 [10] 2.5 Lim [14] Lim Maskeri [15] Eclipse JDT-Core Application Program Interface(API) [16] OS API API API API 2 Windows API 3 ProGuard: 4 DashO: 5 Allatori: 11
13 , Lim Java C# 12
14 Step 1. Step 2. Step 3. Step
15 !"!"#$%&'()*+, #$, #%, 3456,-7./ / /888, &'()"$*"-./012, 89898, #$, -./0$, -./0%, -./0+, #%, -./0$, -./0%, 89898, -./06, 7-$/0%0$-%48/888, &'()"%*":;<, #$, -./0$, -./0%, 89898, -./0+, #%, -./0$, -./0%, 89898, -./06, &'()"8*"-./0=5>?%, #$, -./0$, -./0%, 89898, -./0+, #%, -./0$, -./0%, 89898, -./06, 2: 14
16 "#$%! &'()+'*,(!*-)*! &'().'*/(!*/)*! &'()0'*1(!*/)*!!!!"!!!!!!!!!#!!!!!$!!!!!%!!!!!!!!&!!!!!&!!!$!!!!!!!!!%!!!!!#! +,-! "'#(!$)*! %'&(!&)*! $'%(!#)*! 3: ( ) 3 A(X, Y) A(X, Y) A X Y (1, 2); B(Z, Z) A(X, Y) B Z 3 4 3(4, 4); C(W, Z) 2(3, 1); 3.3 ( ) [24]
17 α N MC (α) α β N match (α, β) α β phase similarity(α, β) = N match (α, β) max(n MC (α), N MC (β)) 4 2 Algorithm Algorithm 2 2 Approximate-DP(Algorithm 3 5 ) [24] Figure 9.32 MatchPhase MatchPhase sort by descending similarities(algorithm 4 3 ) MatchPhase 2 Step 1. (Algorithm 3) 2 Approximate-DP MatchPhase Step 2. (Algorithm 4) MP 0 MP Algorithm 2 procedure MatchingPhases ( in P, P : set of phases; in threshold: integer; out MP : set of MatchPhase); 1. list of MatchPhase MP 0 CalcPhaseSimilarity(P, P, threshold) 2. set of MatchPhase MP OneOnOneP hase(mp 0 ) 16
18 Algorithm 3 procedure CalcPhaseSimilarity ( in P, P : set of phases; in threshold: integer; out MP : list of MatchPhase); 1. list of MatchPhase MP ϕ 2. for p in P do 3. for p in P do 4. if len(p) threshold len(p ) threshold then 5. MatchPhase mp Approximate-DP(p, p ) 6. MP MP.add(mp) 7. end if 8. end for 9. end for Algorithm 4 procedure OneOnOnePhase ( in MP 0 : list of MatchPhase; out MP : set of MatchPhase); 1. set of MatchPhase MP ϕ 2. set of phases Matched ϕ 3. MP 0 sort by descending similarities(mp 0 ) 4. for MatchPhase mp in MP 0 do 5. if Matched.contains(mp.p) Matched.contains(mp.p ) then 6. MP MP.add(mp) 7. M atched M atched.add(mp.p) 8. Matched Matched.add(mp.p ) 9. end if 10. end for 17
19 BC/'#DE"#$%EFG!!"! "#$%"! "#$%#! &'&'&! "#$%$!!#! "#$%"! "#$%#! &'&'&! "#$%&! "#$%8"93:501! -$%'7'$*'&%'7'&.!!"! "#$%"! "#$%#! &'&'&! "#$%$%!!#! "#$%"! "#$%#! &'&'&! "#$%&%! ()*+,-./01' 2345%6!!"! "#$%"! "#$%#! &'&'&! "#$%$%!!#! "#$%"! ()&'"*"! ()&'"*#! &'&'&! ()&'"*$%! "#$%#! ()&'#*"! ()&'#*#! &'&'&! ()&'#*$%! &'&'&! "#$%&%! &'&'&! ()&'&%*"! ()&'&%*#! '&''''&''''&! &'&'&! &'&'&! ()&'&%*$%! -$%'7'&%.!!"! "#$%+"! "#$%+#! &'&'&! "#$%+$%!!#! "#$%,"! "#$%,#! &'&'&! "#$%,$%! 4: 18
20 5 {A B C} {X Y Z} 10 8 Y C Z C X C A Z A B A N MC (A) A B N match (A, B) class similarity(a, B) = 2 N match(a, B) N MC (A) + N MC (B) 6 6 α β 2 α 1 β 1 α 2 β 2 A B A 6 B 5 A B 3 class similarity(a, B) 0.55 A B A B 2 Algorithm Algorithm MatchPhase similarity(algorithm 7 6 ) 19
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
23 MatchClass MatchClass sort by descending similarities(algorithm 8 3 ) MatchClass 3 Step 1. (Algorithm 6) Step 2. (Algorithm 7) similarity MatchClass Step 3. (Algorithm 8) MC 0 MC 7 {< α α > < β β >} A X A 12 X 20 A X 9 class similarity(a, X) 0.56 A X B Y Algorithm 5 procedure MatchingClasses ( in MP : set of MatchPhase; in N: HashMap of the number of methodcall event in each class; out MC: set of MatchClass); 1. HashMap map CountM atchclass(m P ) 2. list of MatchClass MC 0 CalcClassSimilarity(map, N) 3. set of MatchClass MC OneOnOneClass(MC 0 ) 22
24 Algorithm 6 procedure CountMatchClass ( in MP : set of MatchPhase; out map: HashMap of the number of match methodcall count between class pair); 1. HashMap map ϕ 2. for MatchPhase mp in MP do 3. for < class, class > in mp.info do 4. if map.contains(< class, class >) then 5. map map.put(< class, class >, map.get(< class, class >) + 1) 6. else 7. map map.put(< class, class >, 1) 8. end if 9. end for 10. end for Algorithm 7 procedure CalcClassSimilarity ( in map: HashMap of the number of match methodcall count between class pair; in N: HashMap of the number of methodcall event in each class; out MC: list of MatchClass); 1. list of MatchClass MC ϕ 2. for < class, class > in map do 3. integer #methodcall N.get(class) 4. integer #methodcall N.get(class ) 5. integer #matchcall map.get(< class, class >) 6. MatchClass mc similarity(< class, class >, < #methodcall, #methodcall >, #matchcall) 7. MC MC.add(mc) 8. end for 23
25 Algorithm 8 procedure OneOnOneClass ( in MC 0 : list of MatchClass; out MC: set of MatchClass); 1. set of MatchClass MC ϕ 2. set of classes Matched ϕ 3. MC 0 sort by descending similarities(mc 0 ) 4. for mc in MC 0 do 5. if Matched.contains(mc.c) Matched.contains(mc.c ) then 6. MC MC.add(mc) 7. M atched M atched.add(mc.c) 8. Matched Matched.add(mc.c ) 9. end if 10. end for 24
26 )*34"+",+-./012%&'(34356!!"! %&'(0! %&'(1!!%! %&'(02! %&'(12! "#$3789:;<=>! 78$! "%! 6! 6! 78$! %(! ",!!%! "#$&! "#$'!!"! "#$5! "#$#! "#$$! 6! %!,!.! /! "#$&! "#$'!!"! "#$5! "#$#! "#$$! ()*+! ()",! ()%-! ()%.! ()+"! E*34"+",+-./012"#$34356!!"! "#$5! "#$#!!%! "#$&! "#$'! 7: 25
27 Android ICCA 6 Gemini ICCA Virgo Gemini Virgo Gemini Java 120 Virgo Java 26 ProGuard 7 ProGuard 2.4 Amida 1: Gemini Virgo Gemini 21, , Virgo 15, , ICCA: 7 ProGuard ( Android 26
28 4.1.1 Gemini Virgo ProGuard 1 Xeon 2.66GHz CPU Gemini Virgo 1 ProGuard 2 Gemini Virgo 2 ( ) ( ) CCFinder[9] ProGuard jar Java Decompiler 8 2: ( ) (CCFinder) Gemini ( ) Gemini ( ) Virgo ( ) Virgo ( ) Java Decompiler: 27
29 2 1 if-else Java [4] CCFinder Android Amida Android Android [8] smali Android smali smali 9 return ID RealCalc Plus 10 Quartic Software Google Play smali: 10 RealCalc Plus: RealCalcPlus 11 Google Play: 12 : 28
30 RealCalc Plus Quartic Software Google Play RealCalc Plus RealCalc Plus 3: RealCalc Plus sin cos tan sinh cosh tanh 1 sin 1 cos 1 tan 1 hyp xy x y x 2 x % 1 x x3 3 x ln e x log 10 x EXP MOD n! n C r n P r π e 0! log(0) 0 P 0 0 C 0 y x tan( π 2 ) 4: RealCalc Plus(Google Play) 76, RealCalc Plus( ) 73, :
31 !#,"!#+"!#*"!#)"! " #!#'"!#&" %&'$ ()*+$!#("!#%"!#$"!"!"!-!#$"!#$-!#%"!#%-!#&"!#&-!#'"!#'-!#("!#(-!#)"!#)-!#*"!#*-!#+"!#+-!#,"!#,-$#!"!"#$ 8: Android DUMAPIC 13 INFOLIFE 14 2 INFOLIFE [3] INFOLIFE INFOLIFE LLC 2011/07/14 21:28: Hi, I m Eong from INFOLIFE LLC. Please pay attention that we developed the English version first. We don t know Japanese, the translation was done by 3rd party. The name of these two app may be the same. But the icon, the content and the functions are very different. Apple uses APP STORE to sell apps, Amazon can also use APP STORE for app selling. The name doesn t mean everything. You may ask why we need the internet permission, because we do collect crash information to improve the app. As a company from USA, we are trying to make better apps to make 13 (DUMAPIC): historyeraser 14 (INFOLIFE): eraser 30
32 people s life easier. Please don t just denigrate without any deep investigation. (We keep the right to sue you.) DUMAPIC INFOLIFE 6: (DUMAPIC) Gmail (INFOLIFE) Gmail SMS/MMS SMS/MMS SMS/MMS SMS/MMS 7: (DUMAPIC) 1,231 5 (INFOLIFE) 1, :
33 !#($"!#("!#'$"! "#!#'"!#&$"!#&" %&'$ ()*+$!#%$"!#%"!#!$"!"!"!)!#%"!#%)!#&"!#&)!#'"!#')!#("!#()!#$"!#$)!#*"!#*)!#+"!#+)!#,"!#,)!#-"!#-)%#!"!"#$ 9: 32
34 Android ( ) ( ) Android 15 Android API Android 5.2 Amida 15 Android [8] 33
35 Android Amida Amida 2 ICCA Gemini Virgo Java
36 35
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39 [1] I. D. Baxter, A. Yahin, L. Moura, M. S. Anna, and L. Bier. Clone Detection Using Abstract Syntax Trees. In Proc. of ICSM 1998, pp , [2] C. Collberg, C. Thomborson, and D. Low. A taxonomy of obfuscating transformations. Technical Report 148, Department of Computer Science, University of Auckland, Blog. /. [4] Y. Higo, T. Kamiya, S. Kusumoto, and K. Inoue. Method and implementation for investigating code clones in a software system. Information and Software Technology, Vol. 49, No. 9-10, pp , [5],,.., Vol. J91-D, No. 6, pp , [6] S. Horwitz, T. Reps, and D. Binkley. Interprocedural slicing using dependence graphs. ACM TOPLAS, Vol. 12, pp , [7] L. Jiang, G. Misherghi, Z. Su, and S. Glondu. DECKARD: Scalable and accurate tree-based detection of code clones. In Proc. of ICSE 2007, pp , [8] JUMPERZ.NET Blog. /. [9] T. Kamiya, S. Kusumoto, and K. Inoue. CCFinder: A multilinguistic token-based code clone detection system for large scale source code. IEEE Trans. Softw. Eng., Vol. 28, No. 7, pp , [10],,,.., Vol. 28, No. 1, pp , [11] M. Kim, L. Bergman, T. Lau, and D. Notkin. An Ethnographic Study of Copy and Paste Programming Practices in OOPL. In Proc. of ISESE 2004, pp , [12] J. Krinke. Identifying similar code with program dependence graphs. In Proc. of WCRE 2001, pp ,
40 [13] H. Lieberman and C. Hewitt. A real-time garbage collector based on the lifetimes of objects. CACM, Vol. 26, No. 6, pp , [14] H. Lim, H. Park, S. Choi, and T. Han. A method for detecting the theft of Java programs through analysis of the control flow information. Information and Software Technology, Vol. 51, No. 9, pp , [15] G. Maskeri, D. Karnam, S. A. Viswanathan, and S. Padmanabhuni. Version History Based Source Code Plagiarism Detection in Proprietary Systems. In ICSM 2012, pp , [16],,,,. API., Vol. J89-D, No. 8, pp , [17] E. Raymond and R. Landley. OSI position paper on the SCO-vs.-IBM complaint, [18] A. Sæbjørnsen, J. Willcock, T. Panas, D. Quinlan, and Z. Su. Detecting code clones in binary executables. In Proc. of ISSTA 2009, pp , [19],,,. Java DonQuixote. FOSE 2006, pp , [20],,,,.., Vol. 24, No. 3, pp , [21] T. Ueno. Pocketmascot, ne.jp/wince/about_pocketmascot/about_pocketmascot.html. [22],,.., Vol. 51, No. 12, pp , [23] R. Wettel and R. Marinescu. Archeology of code duplication: recovering duplication chanins from small duplication fragments. In Proc. of SYNASC 2005, pp , [24] R. B. Yates and B. R. Neto. Modern Information Retrieval Second edition. Addison Wesley,
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1,a) 1,b) 1,c) 1,d) Discussion on Code Clone Modification Tool for Development Process with Version Control System Yusuke Fujihara 1,a) Kenji Fujiwara 1,b) Norihiro Yoshida 1,c) Hajimu Iida 1,d) Abstract:
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22 A person recognition using color information 1110372 2011 2 13 ,,.,.,,.,.,.,.,,.,..,,,, i Abstract A person recognition using color information Tatsumo HOJI Recently, for the purpose of collection of
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LMS 1 2 2 LMS Blended-Learning CS PC Web LMS MOOC CS PC LMS LMS Requested Features for Mobile Learning Application dedicated to LMS Toshiyuki Kamada 1 Yasushi Kodama 2 Yuki Terawaki 2 Abstract: The blended-learning