Malware detection model based on classifying system calls and code attributes: a proof of concept
by Malik F. Saleh
International Journal of Electronic Security and Digital Forensics (IJESDF), Vol. 11, No. 2, 2019

Abstract: The process of malware detection involves static code analysis and dynamic analysis. Both methods have limitations. This research tried to bridge the gap between the two methods by dynamically predicting the risk before the static analysis. The proof-of-concept examined the code of known malwares and concluded that five characteristics of the code will predict the risk of any executable file, namely, the system function, encryption, code obfuscation, stalling code, and checking for the debugger environment. The proof-of-concept validates the effectiveness of the model. It shows 96% success and limited false-positives results.

Online publication date: Fri, 25-Jan-2019

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