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MalDozer: Automatic framework for android malware detection using deep learning - 2018

Research Area:  Machine Learning

Abstract:

Android OS experiences a blazing popularity since the last few years. This predominant platform has established itself not only in the mobile world but also in the Internet of Things (IoT) devices. This popularity, however, comes at the expense of security, as it has become a tempting target of malicious apps. Hence, there is an increasing need for sophisticated, automatic, and portable malware detection solutions. In this paper, we propose MalDozer, an automatic Android malware detection and family attribution framework that relies on sequences classification using deep learning techniques. Starting from the raw sequence of the apps API method calls, MalDozer automatically extracts and learns the malicious and the benign patterns from the actual samples to detect Android malware. MalDozer can serve as a ubiquitous malware detection system that is not only deployed on servers, but also on mobile and even IoT devices. We evaluate MalDozer on multiple Android malware datasets ranging from 1 K to 33 K malware apps, and 38 K benign apps. The results show that MalDozer can correctly detect malware and attribute them to their actual families with an F1-Score of 96%–99% and a false positive rate of 0.06%–2%, under all tested datasets and settings.

Author(s) Name:  ElMouatez BillahKarbab,MouradDebbabi,Abdelouahid Derhab and DjedjigaMouheb

Journal name:  Digital Investigation

Conferrence name:  

Publisher name:  ELSEVIER

DOI:  10.1016/j.diin.2018.01.007

Volume Information:  Volume 24, Supplement, March 2018, Pages S48-S59