A Novel Malware Classification Method Based on Crucial Behavior

Fei Xiao, Yi Sun, Donggao Du, Xuelei Li and Min Luo

Mathematical Problems in Engineering, 2020, vol. 2020, 1-12

Abstract:

Recently, some graph-based methods have been proposed for malware detection. However, current malware is generally characterized by sophisticated behaviors, which makes graph-based malware detection extremely challenging. To address this issue, we propose a graph repartition algorithm by transforming API call graphs into fragment behaviors based on programs’ dynamic execution traces. The proposed algorithm relies on the N -order subgraph ( NSG ) for constructing the appropriate fragment behavior. Moreover, we improve the term frequency-inverse document frequency- (TF-IDF-) like measure and information gain (IG) to extract the crucial N -order subgraph ( CNSG ). This novel behavioral representation and improved extraction method can accurately represent crucial behaviors of malware. Experiments on 4,400 samples demonstrate that the proposed method achieves a high accuracy of 99.75% in malware detection and promising performance of 95.27% in malware classification.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:6804290

DOI: 10.1155/2020/6804290

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