Behavioural API based Virus Analysis and Detection
The growing number of computer viruses and the detection of zero day malware have been the concern for security researchers for a large period of time. Existing antivirus products (AVs) rely on detecting virus signatures which do not provide a full solution to the problems associated with these viruses. The use of logic formulae to model the behaviour of viruses is one of the most encouraging recent developments in virus research, which provides alternatives to classic virus detection methods. To address the limitation of traditional AVs, we proposed a virus detection system based on extracting Application Program Interface (API) calls from virus behaviours. The proposed research uses a temporal logic and behaviour-based detection mechanism to detect viruses at both user and kernel level. Interval Temporal Logic (ITL) will be used for virus specifications, properties and formulae based on the analysis of API calls representing the behaviour of computer viruses.
Citation : Al Amro, S. and Cau, A. (2012) Behavioural API based Virus Analysis and Detection. International Journal of Computer Science and Information Security, 10 (5), pp. 14-22
ISSN : 1947-5500
Research Group : Software Technology Research Laboratory (STRL)
Peer Reviewed : Yes