Are We There Yet? Unraveling the State-of-the-Art Graph Network Intrusion Detection Systems
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908284987899904 |
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| author | Wang, Chenglong Zheng, Pujia Gui, Jiaping Hua, Cunqing Hassan, Wajih Ul |
| author_facet | Wang, Chenglong Zheng, Pujia Gui, Jiaping Hua, Cunqing Hassan, Wajih Ul |
| contents | Network Intrusion Detection Systems (NIDS) are vital for ensuring enterprise security. Recently, Graph-based NIDS (GIDS) have attracted considerable attention because of their capability to effectively capture the complex relationships within the graph structures of data communications. Despite their promise, the reproducibility and replicability of these GIDS remain largely unexplored, posing challenges for developing reliable and robust detection systems. This study bridges this gap by designing a systematic approach to evaluate state-of-the-art GIDS, which includes critically assessing, extending, and clarifying the findings of these systems. We further assess the robustness of GIDS under adversarial attacks. Evaluations were conducted on three public datasets as well as a newly collected large-scale enterprise dataset. Our findings reveal significant performance discrepancies, highlighting challenges related to dataset scale, model inputs, and implementation settings. We demonstrate difficulties in reproducing and replicating results, particularly concerning false positive rates and robustness against adversarial attacks. This work provides valuable insights and recommendations for future research, emphasizing the importance of rigorous reproduction and replication studies in developing robust and generalizable GIDS solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20281 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Are We There Yet? Unraveling the State-of-the-Art Graph Network Intrusion Detection Systems Wang, Chenglong Zheng, Pujia Gui, Jiaping Hua, Cunqing Hassan, Wajih Ul Cryptography and Security Artificial Intelligence Network Intrusion Detection Systems (NIDS) are vital for ensuring enterprise security. Recently, Graph-based NIDS (GIDS) have attracted considerable attention because of their capability to effectively capture the complex relationships within the graph structures of data communications. Despite their promise, the reproducibility and replicability of these GIDS remain largely unexplored, posing challenges for developing reliable and robust detection systems. This study bridges this gap by designing a systematic approach to evaluate state-of-the-art GIDS, which includes critically assessing, extending, and clarifying the findings of these systems. We further assess the robustness of GIDS under adversarial attacks. Evaluations were conducted on three public datasets as well as a newly collected large-scale enterprise dataset. Our findings reveal significant performance discrepancies, highlighting challenges related to dataset scale, model inputs, and implementation settings. We demonstrate difficulties in reproducing and replicating results, particularly concerning false positive rates and robustness against adversarial attacks. This work provides valuable insights and recommendations for future research, emphasizing the importance of rigorous reproduction and replication studies in developing robust and generalizable GIDS solutions. |
| title | Are We There Yet? Unraveling the State-of-the-Art Graph Network Intrusion Detection Systems |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2503.20281 |