Comparison of Interaction Profiling Bipartite Graph Mining and Graph Neural Network for Malware-Control Domain Detection

Tzung Han Jeng, Chien Chih Chen, Yu Lung Tsai, Yi Ming Chen

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

In the rapidly evolving realm of cybersecurity, the detection of malicious domains stands as a critical challenge. Traditional methodologies, reliant on expert-driven feature engineering, are increasingly strained against the dynamic tactics of cyber-criminals. This paper introduces a novel approach utilizing Graph Neural Networks (GNNs) to enhance the detection of malicious domains. By leveraging un-supervised representation learning techniques, such as Deep Graph Infomax, we transform network traffic data into graph data models, thereby reducing reliance on domain expert input for feature identification. Our method demonstrates marked improvements in domain name classification using real-world data. This research contrasts the new data-driven approach with traditional methods, high-lighting its superior adaptability, reduced dependency on expert knowledge, and potential for broader application. The findings underscore the efficacy of GNNs in cybersecurity and open avenues for future research in applying advanced ma-chine learning techniques to cyber threat detection.

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主出版物標題Proceedings of the 2024 International Conference on Information Technology, Data Science, and Optimization, I-DO 2024
發行者Association for Computing Machinery
頁面12-19
頁數8
ISBN(電子)9798400709180
DOIs
出版狀態已出版 - 22 5月 2024
事件2024 International Conference on Information Technology, Data Science, and Optimization, I-DO 2024 - Taipei, Taiwan
持續時間: 22 5月 202424 5月 2024

出版系列

名字ACM International Conference Proceeding Series

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???event.eventtypes.event.conference???2024 International Conference on Information Technology, Data Science, and Optimization, I-DO 2024
國家/地區Taiwan
城市Taipei
期間22/05/2424/05/24

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