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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.17980 |
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| _version_ | 1866929290986127360 |
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| author | Wu, Lijin Lei, Shanshan Liao, Feilong Zheng, Yuanjun Liu, Yuxin Fu, Wentao Song, Hao Zhou, Jiajun |
| author_facet | Wu, Lijin Lei, Shanshan Liao, Feilong Zheng, Yuanjun Liu, Yuxin Fu, Wentao Song, Hao Zhou, Jiajun |
| contents | As the number of IoT devices increases, security concerns become more prominent. The impact of threats can be minimized by deploying Network Intrusion Detection System (NIDS) by monitoring network traffic, detecting and discovering intrusions, and issuing security alerts promptly. Most intrusion detection research in recent years has been directed towards the pair of traffic itself without considering the interrelationships among them, thus limiting the monitoring of complex IoT network attack events. Besides, anomalous traffic in real networks accounts for only a small fraction, which leads to a severe imbalance problem in the dataset that makes algorithmic learning and prediction extremely difficult. In this paper, we propose an EG-ConMix method based on E-GraphSAGE, incorporating a data augmentation module to fix the problem of data imbalance. In addition, we incorporate contrastive learning to discern the difference between normal and malicious traffic samples, facilitating the extraction of key features. Extensive experiments on two publicly available datasets demonstrate the superior intrusion detection performance of EG-ConMix compared to state-of-the-art methods. Remarkably, it exhibits significant advantages in terms of training speed and accuracy for large-scale graphs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17980 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning Wu, Lijin Lei, Shanshan Liao, Feilong Zheng, Yuanjun Liu, Yuxin Fu, Wentao Song, Hao Zhou, Jiajun Cryptography and Security Machine Learning As the number of IoT devices increases, security concerns become more prominent. The impact of threats can be minimized by deploying Network Intrusion Detection System (NIDS) by monitoring network traffic, detecting and discovering intrusions, and issuing security alerts promptly. Most intrusion detection research in recent years has been directed towards the pair of traffic itself without considering the interrelationships among them, thus limiting the monitoring of complex IoT network attack events. Besides, anomalous traffic in real networks accounts for only a small fraction, which leads to a severe imbalance problem in the dataset that makes algorithmic learning and prediction extremely difficult. In this paper, we propose an EG-ConMix method based on E-GraphSAGE, incorporating a data augmentation module to fix the problem of data imbalance. In addition, we incorporate contrastive learning to discern the difference between normal and malicious traffic samples, facilitating the extraction of key features. Extensive experiments on two publicly available datasets demonstrate the superior intrusion detection performance of EG-ConMix compared to state-of-the-art methods. Remarkably, it exhibits significant advantages in terms of training speed and accuracy for large-scale graphs. |
| title | EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2403.17980 |