NDLI: AndroSimilar: robust statistical feature signature for Android malware detection

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AndroSimilar: robust statistical feature signature for Android malware detection

Content Provider	ACM Digital Library
Author	Faruki, Parvez Bharmal, Ammar Ganmoor, Vijay Laxmi, Vijay Gaur, M. S.
Abstract	Android Smartphone popularity has increased malware threats forcing security researchers and AntiVirus (AV) industry to carve out smart methods to defend Smartphone against malicious apps. Robust signature based solutions to mitigate threats become necessary to protect the Smartphone and confidential user data. In this paper we present AndroSimilar, a robust approach which generates signature by extracting statistically improbable features, to detect malicious Android apps. Proposed method is effective against code obfuscation and repackaging, widely used techniques to evade AV signature and to propagate unseen variants of known malware. AndroSimilar is a syntactic foot-printing mechanism that finds regions of statistical similarity with known malware to detect those unknown, zero day samples. Syntactic file similarity of whole file is considered instead of just opcodes for faster detection compared to known fuzzy hashing approaches. Results demonstrate robust detection of variants of known malware families. Proposed approach can be refined to deploy as Smartphone AV.
Starting Page	152
Ending Page	159
Page Count	8
File Format	PDF
ISBN	9781450324984
DOI	10.1145/2523514.2523539
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-11-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Improbable features Code obfuscation Android malware Similarity digest Statistical features
Content Type	Text
Resource Type	Article

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