Research on Intrusion Detection Method Based on Recurrent Neural Network

Intrusion Detection System (IDS) scans the network or system for intrusion activities or security vulnerabilities, protects the network or host from damage, and detects intrusion behaviors by looking for the characteristics of known attacks or differences from normal activities. Compared with traditional machine learning methods, deep learning algorithms are more effective in intrusion detection. This paper presents a deep learning method based on Long Short-Term Memory (LSTM) to detect attacks. Principal Component Analysis (PCA) and Mutual Information (MI) are used for reduction and feature selection. The proposed intrusion detection method is tested on the KDDCUP '99 dataset, and the results show that the intrusion detection method based on deep learning and PCA achieves better training and testing accuracy in binary classification and multi-feature classification.