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IoT malware classification based on reinterpreted function-call graphs
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DOI：10.1016/j.cose.2022.103060.png)
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Various malware and cyberattacks have arisen along with the proliferation of IoT devices. The evolving malware targeting IoT devices calls forth effective and efficient solutions to protect vulnerable IoT de-vices from being compromised. In this paper, we investigate the feasibility of a state-of-the-art graph embedding method, graph2vec, for performing family classification for IoT malware, with promising re-sults reported. To further improve the generalization performance of the classifiers based on graph2vec- extracted features, we propose two new mechanisms to improve the quality of feature representation. First, we unify user-defined function calls by reinterpreting the opcode sequences therein to better cap-ture the semantics of the function-call relationship in malware binaries. Then, we integrate literal infor-mation into the graph2vec embedding of the function call graph to achieve better discriminant ability. To prove the effectiveness of the proposed scheme, we carried out performance comparison on a large-scale dataset containing more than 108K malware binaries collected from seven CPU architectures. The accu-racy rates obtained by five widely adopted classifiers on malware family classification are improved by 2%, on average, by adopting the two proposed mechanisms. Specifically, when combined with the pro-posed approach, the support vector machine classifier obtained an accuracy rate of 98.88% on malware family classification, outperforming known function-call-graph (FCG)-based methods and previous work on static malware analysis.(c) 2022 Elsevier Ltd. All rights reserved.
Keyword:
Cybersecurity
IoT malware analysis
Machine learning
Static analysis
Graph embedding
期刊
C
IF:
5.4
论文数:
4.6K
被引数:
1.4W
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