Intrusion Detection Using Few-shot Learning Based on Triplet Graph Convolutional Network

Abstract

Machine learning and deep learning methods have been widely used in network intrusion detection, most of which are supervised intrusion detection methods, which need to train a lot of marked data. However, in some cases, a small amount of exception data is hidden in a large amount of exception data, making methods that require a large amount of the same markup data to learn features invalid. In order to solve this problem, this paper proposes an innovative method of small sample network intrusion detection. The innovation point is that network data is modeled as graph structure to effectively mine the correlation features between data samples, and by comparing the distance similarity, the triplet network structure is used to detect anomalies. The triplet network is composed of triplet graph convolutional neural network which shares the same parameters and is trained by providing triplet samples to the network. Experiments on network traffic datasets CSE-CIC-IDS2018 and UNSW-NB15 as well as system status monitoring datasets verify the effectiveness of the proposed method in network intrusion detection of small samples.