Network Security Intrusion Detection Methods Combining Optimization Algorithms and Neural Networks

Abstract

Due to the diverse and volatile nature of today's network attacks, it is difficult to achieve satisfactory results using a single detection method. This project plans to study the network intrusion detection algorithm based on Semi-Supervised Learning and active learning to improve its accuracy and effectively intercept various network attacks. First, network attack data is collected, attack characteristics are extracted, and Semi-Supervised Learning method is used to cluster attack data. Secondly, by studying the clustering data, a network intrusion detection classifier based on BP neural network was constructed, and the construction process of the classifier was improved. On this basis, the effectiveness of the algorithm was verified through simulation experiments using standard datasets. The experimental results show that the combination rate of the optimized algorithm for network intrusion detection and neural network methods is over 95%, which can meet the actual accuracy requirements of network security protection. This is because the method proposed by the author combines semi supervised technology with BP neural network, which can effectively identify different types of network intrusion behavior, thereby reducing the missed detection rate and false alarm rate of network intrusion detection, and has significant advantages.