# Network Anomaly Traffic Detection via Multi-view Feature Fusion

arXiv: https://arxiv.org/abs/2409.08020 · HTML: https://mlsanity.com/paper/2409.08020/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 12 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT](https://mlsanity.com/paper/2103.16329/) (2021)
- [NetGPT: Generative Pretrained Transformer for Network Traffic](https://mlsanity.com/paper/2304.09513/) (2023)
- [NetMamba: Efficient Network Traffic Classification via Pre-training Unidirectional Mamba](https://mlsanity.com/paper/2405.11449/) (2024)
- [Network Traffic Anomaly Detection Using Recurrent Neural Networks](https://mlsanity.com/paper/1803.10769/) (2018)

## Built on by (most cited first, 2 of 2)

- [A Survey on Foundation-Model-Based Industrial Defect Detection](https://mlsanity.com/paper/2502.19106/) (2025)
- [Hierarchical Local-Global Feature Learning for Few-shot Malicious Traffic Detection](https://mlsanity.com/paper/2504.03742/) (2025)

## Abstract (arXiv)

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.