Efficient signature based malware detection on mobile devices

Article type: Research Article

Authors: Venugopal, Deepak | Hu, Guoning

Affiliations: Nokia Inc, 6000 Connection Dr, Irving, TX 75039, USA. E-mail: deepak.venugopal@nokia.com | Truveo Inc, An AOL Company, 333 Bush Street, San Francisco, CA 94104, USA. E-mail: guoninghu@aol.com

Note: [] Corresponding author

Abstract: The threat of malware on mobile devices is gaining attention recently. It is important to provide security solutions to these devices before these threats cause widespread damage. However, mobile devices have severe resource constraints in terms of memory and power. Hence, even though there are well developed techniques for malware detection on the PC domain, it requires considerable effort to adapt these techniques for mobile devices. In this paper, we outline the considerations for malware detection on mobile devices and propose a signature based malware detection method. Specifically, we detail a signature matching algorithm that is well suited for use in mobile device scanning due to its low memory requirements. Additionally, the matching algorithm is shown to have high scanning speed which makes it unobtrusive to users. Our evaluation and comparison study with the well known Clam-AV scanner shows that our solution consumes less than 50% of the memory used by Clam-AV while maintaining a fast scanning rate.

Keywords: Malware, signature detection, mobile and security

Journal: Mobile Information Systems, vol. 4, no. 1, pp. 33-49, 2008