Hierarchical Neural Networks for Detecting Anomalous Traffic Flows
Seung‐Jin Ryu, Wooyoung Go, Daewoo Lee, Hanjun Yoon · 2019
Intrusion Detection System (IDS) is designed to protect network assets by inspecting specified region or properties of network traffic. To represent hierarchical traffic flows comprehensively, this paper proposes a deep learning architecture for anomaly detection. We employ CNN model to automatically extract an adequate feature vector for each message composed of unidirectional sequence of packets. Based upon the CNN structure, we build BiLSTM with attention mechanism to summarize the sequential message vectors as a representative flow feature vector. Empirical evidence from reduced UNSW-NB15 dataset indicates the superior performance of the proposed method for detecting anomalous traffic flows.