Zero-day malware detection based on supervised learning algorithms of API call signatures
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Detecting malicious behaviour using supervised learning algorithms of the function calls
International Journal of Electronic Security and Digital Forensics
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Along with the popularization of computers, especially the wide use of Internet, malicious code in recent years has presented a serious threat to our world. In this paper, through the analysis against the suspicious behaviors of vicious program by function calls, we present an approach of malware detection which is based on analysis and distilling of representative characteristic and systemic description of the suspicious behaviors indicated by the sequences of APIs called under Windows. Based on function calls and control flow analysis, according to the identification of suspicious behavior, the technique implements a strategy of detection from malicious binary executables.