【摘要】 为降低网络入侵的风险，提出一种基于小生境遗传算法的网络入侵节点智能检测方法.首先，针对网络入侵的攻击行为进行聚合处理，利用双人攻防博弈模型分析网络的攻防状态，通过比对攻击与防御的效用强度，对网络的安全性进行全面分析，再根据分析结果，通过卷积神经网络实现对攻击源的定位.其次，基于粗糙集理论，利用小生境遗传算法确定网络入侵节点检测的适应度函数，根据网络入侵节点智能检测规则，建立网络入侵节点智能检测模型，获得最终的检测结果.实验结果表明，该方法可有效提升对入侵攻击源的定位准确性和入侵节点检测准确性，该方法检测结果的宏F₁分数大于0.96,表明该方法可有效实现设计预期.更多还原

【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.更多还原