MATTER: A Multi-Level Attention-Enhanced Representation Learning Model for Network Intrusion Detection
Jinghong Lan, Yanan Li, Bo Li, Xudong Liu · 2022
Network Intrusion Detection Systems (NIDSs) play a crucial role in safeguarding the security of protected computer networks. Although numerous machine learning algorithms, especially deep learning algorithms, have achieved remarkable results, their generalization ability is limited due to the following critical challenges. First, most of existing methods heavily rely on the handcrafted features extracted from packets or network flows. Second, few studies have been devoted to adaptively highlighting the characteristics of certain traffic features and thus extracting discriminative representations from input network data. In this paper, we propose a Multi-level ATTention-enhanced rEpresentation leaRning model (MATTER) to address the aforementioned challenges. Specifically, a multi-scale Convolutional Neural Network (CNN) is employed to extracted representations from the raw packet content of a network flow. Then, a multi-level attention module with spatial, channel and temporal attention mechanisms is leveraged to enhance the discrimination of the extracted features. Extensive experiments on two benchmark datasets demonstrate that our proposed MATTER is superior to other state-of-the-art approaches in terms of both accuracy and F1 score.