Design of a Lightweight Network Intrusion Detection System Based on Artificial Intelligence Technology
He Li · Journal of Cyber Security and Mobility · 2024
Network security issues have become crucial with the boost of Internet of Things technology. To detect lightweight network intrusion, this research improves the population initialization mode of given the genetic algorithm given the Pearson correlation coefficient and constructs a feature selection model. In view of the one-dimensional convolutional neural network model, it introduces the gated cyclic unit neural network model. It uses pruning operations to realize the lightweight of the model and build an intrusion detection model. The results showed that the accuracy, detection rate, and time average of the improved genetic algorithm were 79.55%, 90.32%, and 189.4 s, which were 14.87%, 30.35%, and 33.05% higher than the traditional genetic algorithm model, respectively. The intrusion detection model has achieved an accuracy of 95.0%, and the loss function value is 0.15. Compared with other deep learning models, it is more robust and performs better in intrusion detection. The average accuracy of the model testing after lightweight is 88.6%, the average detection rate is 98.12%, and the average testing time is 82 s, which improves the model’s performance compared to before lightweight. This study could markedly enhance the accuracy and detection rate of lightweight network intrusion detection, with higher detection efficiency and better performance, and possesses an essential influence in improving network security.