Deep learning for image-based mobile malware detection

• Published in: Journal of Computer Virology and Hacking Techniques Vol. 16; no. 2; pp. 157 - 171
• Main Authors: Mercaldo, Francesco, Santone, Antonella
• Format: Journal Article
• Language: English
• Published: Paris Springer Paris 01.06.2020; Springer Nature B.V
• Subjects: Computer Science; Deep learning; Image detection; Malware; Original Paper; Deep learning; Apple; Machine learning; Classification; Malware; Security; Image; Artificial intelligence; Android
• ISSN: 2263-8733; 2263-8733
• DOI: 10.1007/s11416-019-00346-7

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.