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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/2409.08020 |
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| _version_ | 1866909884980658176 |
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| author | Hao, Song Fu, Wentao Chen, Xuanze Jin, Chengxiang Zhou, Jiajun Yu, Shanqing Xuan, Qi |
| author_facet | Hao, Song Fu, Wentao Chen, Xuanze Jin, Chengxiang Zhou, Jiajun Yu, Shanqing Xuan, Qi |
| contents | Traditional anomalous traffic detection methods are based on single-view analysis, which has obvious limitations in dealing with complex attacks and encrypted communications. In this regard, we propose a Multi-view Feature Fusion (MuFF) method for network anomaly traffic detection. MuFF models the temporal and interactive relationships of packets in network traffic based on the temporal and interactive viewpoints respectively. It learns temporal and interactive features. These features are then fused from different perspectives for anomaly traffic detection. Extensive experiments on six real traffic datasets show that MuFF has excellent performance in network anomalous traffic detection, which makes up for the shortcomings of detection under a single perspective. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08020 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Network Anomaly Traffic Detection via Multi-view Feature Fusion Hao, Song Fu, Wentao Chen, Xuanze Jin, Chengxiang Zhou, Jiajun Yu, Shanqing Xuan, Qi Machine Learning Traditional anomalous traffic detection methods are based on single-view analysis, which has obvious limitations in dealing with complex attacks and encrypted communications. In this regard, we propose a Multi-view Feature Fusion (MuFF) method for network anomaly traffic detection. MuFF models the temporal and interactive relationships of packets in network traffic based on the temporal and interactive viewpoints respectively. It learns temporal and interactive features. These features are then fused from different perspectives for anomaly traffic detection. Extensive experiments on six real traffic datasets show that MuFF has excellent performance in network anomalous traffic detection, which makes up for the shortcomings of detection under a single perspective. |
| title | Network Anomaly Traffic Detection via Multi-view Feature Fusion |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.08020 |