Enhancing Malware Detection Accuracy through Graph Based Model
K. Muthumanickam, E. Ilavarasan
Journal of Advances in Mathematics and Computer Science · pp. 2237–2250 · Published 13 Jun 2014
10.9734/BJMCS/2014/10722Abstract
Malicious malware is a serious threat to end-user in the Internet. Run-time analysis of a program execution behavior is widely used to classify malware’s activities especially when its signature is not obtainable. Towards this end, most of the existing run-time malware detection techniques make use of the information available in the Application Programming Interface call sequence in Windows platform. This paper suggests a novel malware revealing model based on graph model by capturing system calls during the execution of a suspected executable. The implementation results confirm that the proposed call graph model has better detection accuracy rate and also solves the scalability problem when it is compared to existing methods.
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Cited by 2
P. Pandiaraja, K. Muthumanickam, R. Palani Kumar · Lecture Notes in Networks and Systems · 2023
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