Networks refer to a collection of interconnected elements or nodes that communicate and share resources with each other. These elements can be various entities, such as computers, devices, or systems, and the connections between them may involve physical links (e.g., cables) or logical connections (e.g., wireless communication).

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Heterogeneous Graph Matching Networks for Unknown Malware Detection

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Information systems have widely been the target of malware attacks. Traditional signature-based malicious program detection algorithms can only detect known malware and are prone to evasion techniques such as binary obfuscation, while behavior-based approaches highly rely on the malware training samples and incur prohibitively high training cost. To address the limitations of existing techniques, we propose MatchGNet, a heterogeneous Graph Matching Network model to learn the graph representation and similarity metric simultaneously based on the invariant graph modeling of the program’s execution behaviors. We conduct a systematic evaluation of our model and show that it is accurate in detecting malicious program behavior and can help detect malware attacks with less false positives. MatchGNet outperforms the state-of-the-art algorithms in malware detection by generating 50% less false positives while keeping zero false negatives.

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