The growing in use of smart mobile devices for everyday applications has stimulated the spread of mobile malware, especially on popular mobile platforms. As a consequence, malware detection becomes ever more critical in sustaining the mobile market and providing a better user experience. In this paper, we review the existing malware and detection schemes. Using real-world malware samples with known signatures, we evaluate four popular commercial anti-virus tools and our data shows that these tools can achieve high detection accuracy. To deal with the new malware with unknown signatures, we study the anomaly based detection using decision tree algorithm. We evaluate the effectiveness of our detection scheme using malware and legitimate software samples. Our data shows that the detection scheme using decision tree can achieve a detection rate up to 90% and a false positive rate as low as 10%.

Date Published: 28 May 2013
PDF: 8 pages
Proc. SPIE 8757, Cyber Sensing 2013, 87570H (28 May 2013); doi: 10.1117/12.2016114

Published in SPIE Proceedings Vol. 8757:
Cyber Sensing 2013
Igor V. Ternovskiy; Peter Chin, Editor(s)