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Recently, with the booming development of software industry, more and moremalware variants are designed to perform malicious behaviors. The evolution ofmalware makes it difficult to detect using traditional signature-based methods.Moreover, malware detection has important effect on system security. In thispaper, we present SCGDet, which is a novel malware detection method based onsystem call graph model (SCGM). We first develop a system call pruning method,which can exclude system calls that have little impact on malware detection.Then we propose the SCGM, which can capture the semantic features of run-timeprogram by grouping the system calls based on the reachability relation. We aimto obtain the generic representation of malicious behaviors with similar systemcall patterns. We evaluate the performance of SCGDet using different machinelearning algorithms on the dataset including 854 malware samples and 740 benignsamples. Compared with the traditional n-gram method, the SCGDet has thesmaller feature space, the higher detection accuracy and the lower falsepositives. Experimental results show that SCGDet can reduce the average FPR of14.75% and improve the average Accuracy of 8.887%, and can obtain a TPR of97.44%, an FPR of 1.96% and an Accuracy of 97.78% in the best case.

SCGDet: Malware Detection using Semantic Features Based on Reachability Relation