A Malware Detection Algorithm Based on Multi-view Fusion

Shanqing Guo, Qixia Yuan, Fengbo Lin, Fengyu Wang, Tao Ban

Abstract: One of the major problems concerning information assurance is malicious code. In order to detect them, many existing run-time intrusion or malware detection techniques utilize information available in Application Programming Interface (API) call sequences to discriminate between benign and malicious processes. Although some great progresses have been made, the new research results of ensemble learning make it possible to design better malware detection algorithm. This paper present a novel approach of detecting malwares using API call sequences. Basing on the fact that the API call sequences of a software show local property when doing network, file IO and other operations, we first divide the API call sequences of a malware into seven subsequences, and then use each subsequence to build a classification model. After these building models are used to classify software, their outputs are combined by using BKS and the final fusion results will be used to label whether a software is malicious or not. Experiments show that our algorithm can detect known malware effectively.