A Semi-Supervised Anomaly Network Traffic Detection Framework via Multimodal Traffic Information Fusion

作者：Yu Zheng, Xinglin Lian, Zhangxuan Dang, Chunlei Peng, Chao Yang, Jianfeng Ma · 年份：2023 · DOI：10.1145/3583780.3615214 · 被引用次数：11 · 研究领域：Internet Traffic Analysis and Secure E-voting、Network Security and Intrusion Detection、Anomaly Detection Techniques and Applications

Anomaly traffic detection is a crucial issue in the cyber-security field. Previously, many researchers regarded anomaly traffic detection as a supervised classification problem. However, in real scenarios, anomaly network traffic is unpredictable, dynamically changing and difficult to collect. To address these limitations, we employ anomaly detection setting to propose a novel semi-supervised anomaly network traffic detection framework. It only learns features of normal samples during the training phase. Our framework utilizes low-pass filtering to extract multi-scale low-frequency information from 2-D traffic image. Furthermore, we design a two-stage fusion scheme to incorporate information from original and multi-scale low-frequency traffic image modalities. We conduct experiments on two public datasets: ISCX Tor-nonTor and USTC-TFC2016. The experimental results show that our method outperforms current state-of-the-art anomaly detection methods.