Application of Multi-objective Differential Evolution Algorithm in Computer Network Intrusion Detection System

Abstract

With the rise and development of computer networks, network security has become a major issue, and intrusion detection has emerged as a crucial aspect of network security. However, traditional intrusion detection methods suffer from issues like slow detection rates and low accuracy. This necessitates the exploration and development of optimized algorithms for improving intrusion detection efficiency and accuracy. Recently, the multi-objective differential evolution algorithm has shown promising results in the field of intrusion detection. This paper explored the application of the multi-objective differential evolution algorithm in computer network intrusion detection systems. The experimental results on the KDDCUP'99 dataset demonstrated that the algorithm delivers high levels of precision and recall, outperforming other intrusion detection methods. Therefore, combining the multi-objective differential evolution algorithm with traditional intrusion detection methods can lead to significant improvements in intrusion detection efficiency and accuracy. Further optimization of this algorithm can be required to meet complex intrusion detection requirements. The multi-objective differential evolution algorithm presents exciting prospects, which can help enhance the efficiency and accuracy of intrusion detection, and provide strong technical support for ensuring network security and preventing cyber-attacks.