A Detection Algorithm of Malicious Code Variants based on Extreme Learning
Xiaolong Li, Xinghua Li, Feng Wang, Dongge Zhu · 2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA) · 2022
Malicious code is one of the biggest threats to cyberspace. In all kinds of network security incidents, malicious code is the main attack vector. At present, the traditional malicious code analysis methods based on feature and signature cannot meet the requirement of detection of malicious code. It is urgent for anti-virus manufacturers to explore and research more accurate and efficient malicious code detection and countermeasure mechanism. Through analyzing the format structure information of malicious code samples and combining with the extreme learning machine algorithm in machine learning, this paper studies the malicious code classification technology on the basis of the existing research, and designs the classification algorithm of malicious code family based on extreme learning. Experimental results show that the proposed technical method can effectively realize the classification of malicious code family, with the accuracy of classification more than 90%.