Journal
JOURNAL OF COMPUTER VIROLOGY AND HACKING TECHNIQUES
Volume 16, Issue 2, Pages 157-171Publisher
SPRINGER FRANCE
DOI: 10.1007/s11416-019-00346-7
Keywords
Malware; Android; Apple; Security; Machine learning; Deep learning; Artificial intelligence; Image; Classification
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Current anti-malware technologies in last years demonstrated their evident weaknesses due to the signature-based approach adoption. Many alternative solutions were provided by the current state of art literature, but in general they suffer of a high false positive ratio and are usually ineffective when obfuscation techniques are applied. In this paper we propose a method aimed to discriminate between malicious and legitimate samples in mobile environment and to identify the belonging malware family and the variant inside the family. We obtain gray-scale images directly from executable samples and we gather a set of features from each image to build several classifiers. We experiment the proposed solution on a data-set of 50,000 Android (24,553 malicious among 71 families and 25,447 legitimate) and 230 Apple (115 samples belonging to 10 families) real-world samples, obtaining promising results.
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