异常检测
计算机科学
异常(物理)
人工智能
数据挖掘
领域(数学)
网络安全
机器学习
模式识别(心理学)
计算机安全
物理
数学
纯数学
凝聚态物理
作者
Yujiao Zheng,Xingye Lian,Zhangxuan Dang,Chunlei Peng,Chao Yang,Jianfeng Ma
标识
DOI:10.1145/3583780.3615214
摘要
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.
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