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| Prototype-Based Graph Contrastive Learning for Malware Detection |
| HAN Tong1, WANG Tianfeng1, XU Chengcheng2, GUO Shize1,3, PAN Zhisong1 |
1. College of Command and Control Engineering, Army Engi-neering University of PLA, Nanjing 210007;
2. College of Computer Science, Sichuan University, Chengdu 610065;
3. School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876 |
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Abstract Graph contrastive learning can effectively capture the complex structural dependencies in behavior graphs, and now it is an important tool for malware detection. However, existing methods treat all samples indiscriminately or overemphasize hard samples during training, leading to training oscillation and slow convergence. To address this issue, a prototype-based graph contrastive learning method for malware detection is proposed. Curriculum learning is extended from the instance-selection level to the sample-pair level, and the contrastive difficulty of software semantics is defined by jointly considering the typicality of anchors and negative samples. A dual-threshold dynamic scheduler is designed to enable progressive contrastive training from typical cases to edge cases. Furthermore, a history-guided anchor enhancement mechanism that tracks and reweights insufficiently learned hard anchors is introduced, thereby enhancing the representation capability for hard samples. Experiments on four public malware datasets demonstrate that the proposed method significantly improves training stability and convergence speed.
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Received: 29 July 2026
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| Fund:General Program of National Natural Science Foundation of China(No.62076251) |
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Corresponding Authors:
GUO Shize Ph.D., professor. His research interests include network and system security.
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2026, Vol. 39 
