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2008
ACM

Behavioral detection of malware on mobile handsets

11 years 26 days ago
Behavioral detection of malware on mobile handsets
A novel behavioral detection framework is proposed to detect mobile worms, viruses and Trojans, instead of the signature-based solutions currently available for use in mobile devices. First, we propose an efficient representation of malware behaviors based on a key observation that the logical ordering of an application's actions over time often reveals the malicious intent even when each action alone may appear harmless. Then, we generate a database of malicious behavior signatures by studying more than 25 distinct families of mobile viruses and worms targeting the Symbian OS--the most widely-deployed handset OS--and their variants. Next, we propose a two-stage mapping technique that constructs these signatures at run-time from the monitored system events and API calls in Symbian OS. We discriminate the malicious behavior of malware from the normal behavior of applications by training a classifier based on Support Vector Machines (SVMs). Our evaluation on both simulated and real...
Abhijit Bose, Xin Hu, Kang G. Shin, Taejoon Park
Added 24 Dec 2009
Updated 24 Dec 2009
Type Conference
Year 2008
Where MOBISYS
Authors Abhijit Bose, Xin Hu, Kang G. Shin, Taejoon Park
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