Auxiliary Classifier Generative Adversarial Network Assisted Intrusion Detection System

Abstract

Machine learning is applied widely in the field of intrusion detection at present, the existing intrusion detection algorithm is relatively mature, but how to solve the problem of unbalanced sample data still need further research. Aiming at the problem of detection accuracy and efficiency caused by sample data imbalance in the process of intrusion detection, this paper proposes an intrusion detection method based on the fusion of Auxiliary Classification Adversarial Network (ACGAN) and Graph Neural Network (GNN) (ACGAN-GNN). Firstly, ACGAN is used to expand minority samples in data preprocessing to optimize the dataset, and then the improved heterogeneous graph neural network algorithm is used to model the sample flow relationship in the classification process, so as to improve the detection robustness of minority attack samples and unknown attack samples. The model is evaluated by CICIDS2017 dataset. Compared with similar algorithms, ACGAN-GNN not only has better performance in terms of accuracy, precision, recall and F1-score, but also has higher accuracy against minority or unknown attack types.