An Intrusion Detection Method Based on Attention Mechanism to Improve CNN-BiLSTM Model

• Published in: Computer journal Vol. 67; no. 5; pp. 1851 - 1865
• Main Authors: Shou, Dingyu, Li, Chao, Wang, Zhen, Cheng, Song, Hu, Xiaobo, Zhang, Kai, Wen, Mi, Wang, Yong
• Format: Journal Article
• Language: English
• Published: Oxford University Press 22.06.2024
• Subjects: intrusion detection; recursive feature elimination; attention mechanism; bi-directional long short-term memory network; convolutional neural network; mixed sampling
• ISSN: 0010-4620; 1460-2067
• DOI: 10.1093/comjnl/bxad105

Security of computer information can be improved with the use of a network intrusion detection system. Since the network environment is becoming more complex, more and more new methods of attacking the network have emerged, making the original intrusion detection methods ineffective. Increased network activity also causes intrusion detection systems to identify errors more frequently. We suggest a new intrusion detection technique in this research that combines a Convolutional Neural Network (CNN) model with a Bi-directional Long Short-term Memory Network (BiLSTM) model for adding attention mechanisms. We distinguish our model from existing methods in three ways. First, we use the NCR-SMOTE algorithm to resample the dataset. Secondly, we use recursive feature elimination method based on extreme random tree to select features. Thirdly, we improve the profitability and accuracy of predictions by adding attention mechanism to CNN-BiLSTM. This experiment uses UNSW-UB15 dataset composed of real traffic, and the accuracy rate of multi-classification is 84.5$\%$; the accuracy rate of multi-classification in CSE-IC-IDS2018 dataset reached 98.3$\%$.