An Intrusion Detection Approach Based on Autoencoder and Three-way Decisions

Intrusion detection plays a vital role in computer network security. Intrusion detection is one of the key technologies of network security and needs to be kept under constant attention. As the network environment becomes more and more complex, network intrusion behaviors gradually show diversified and intelligent characteristics, and network intrusion behavior is also becoming more difficult to detect. And the research conducted in the field of network security is also an endless study. In response to these problems, we proposed an intrusion detection method DAE-3WD based on denoising autoencoder and three-way decisions. We hope that our proposed method can effectively improve the performance of intrusion detection. This approach we proposed extracts features from high-dimensional data through denoising autoencoder. Through multiple feature extractions, a multi-granular feature space can be constructed, and then an immediate decision on intrusive or normal behavior is made based on three-way decisions, and further analysis is required for suspicious behaviors. NSL-KDD dataset is used in our experiments. The experiments prove that the proposed approach can extract meaningful features and effectively improve the performance of intrusion detection.