2015
DOI: 10.1016/j.diin.2015.06.001
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Scalable code clone search for malware analysis

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Cited by 25 publications
(13 citation statements)
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“…Farhadi et al [67] proposed a scalable code clone detection approach called ScalClone for malware analysis on the basis of their previous approach, which was for an assemble code clone detection method [12], [76]. Their approach discovers both exact and inexact clones at different token normalization levels using a large-scale assemble code search.…”
Section: ) Token-basedmentioning
confidence: 99%
“…Farhadi et al [67] proposed a scalable code clone detection approach called ScalClone for malware analysis on the basis of their previous approach, which was for an assemble code clone detection method [12], [76]. Their approach discovers both exact and inexact clones at different token normalization levels using a large-scale assemble code search.…”
Section: ) Token-basedmentioning
confidence: 99%
“…Tekchandani et al [37] present git code clone genealogy extraction model by utilizing the DAG data structure and can detect type1 and type2 clones. Farhadi et al [38] present scalclone, a scalable assembly code clone search system by using Zlib, DLL18, Malware297 and DLL1GB datasets. LSH algorithm is applied to find inexact clones.…”
Section: B Lexical Approachesmentioning
confidence: 99%
“…Its license type is Freeware. Similarly, CCFinder is also a detector based on lexical (token based) approach [38], [68], [69]. It is able to identify type 1 and type 2 clones in a short time [39].…”
Section: G Clone Detection Toolsmentioning
confidence: 99%
“…Several researchers have explored the possibility of applying clone detection to detect malware [23][24][25] . Karademir et al [23] used clone detection to identify JavaScript malware in Adobe Acrobat (PDF) files.…”
Section: Motivationmentioning
confidence: 99%
“…Karademir et al [23] used clone detection to identify JavaScript malware in Adobe Acrobat (PDF) files. Both [24] and [25] identify the code clone fragments at binary level. None of them detect the malware at Java source code level.…”
Section: Motivationmentioning
confidence: 99%