Summary of Network Anomaly Traffic Detection Via Multi-view Feature Fusion, by Song Hao et al.
Network Anomaly Traffic Detection via Multi-view Feature Fusion
by Song Hao, Wentao Fu, Xuanze Chen, Chengxiang Jin, Jiajun Zhou, Shanqing Yu, Qi Xuan
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes Multi-view Feature Fusion (MuFF), a novel approach to network anomaly traffic detection that addresses limitations of traditional methods. MuFF models temporal and interactive relationships between packets using two distinct viewpoints, learning features from each perspective. These features are then fused for effective anomaly detection. Experimental results on six real-world datasets demonstrate MuFF’s exceptional performance in identifying anomalous traffic patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make the internet safer by creating a better way to spot unusual network activity. Right now, most methods only look at one type of data at a time. But MuFF looks at two types: how packets are related over time and how they interact with each other. By combining these views, MuFF can detect more kinds of suspicious behavior than previous methods. |
Keywords
» Artificial intelligence » Anomaly detection
