Abstract: In order to reduce the risk of network intrusion, the author proposed an intelligent detection method for network intrusion nodes based on niche genetic algorithm. Firstly, aggregation processing was implemented for the attack behavior of network intrusion,  a two-person attack and defense game model was used to analyze the attack and defense status of the network. By comparing the utility strength of attack and defense, a comprehensive analysis of the network’s security was carried out. Based on the analysis results, the localization of the attack source was achieved through convolutional neural networks. Secondly, based on  rough set theory, the fitness function for network intrusion node detection was determined by using niche genetic algorithm. According to  the intelligent detection rules of network intrusion nodes, an intelligent detection model for network intrusion nodes was established to obtain the final detection results. Experimental results show that this method can effectively improve the accuracy of locating intrusion attack sources and detecting intrusion nodes. The macro F₁  score of the detection results of this method is greater than 0.96, indicating that this method can effectively achieve the design expectations.

Key words: niche genetic algorithm, network intrusion, intrusion node, rough set theory, fitness function, intrusion detection