基于免疫智能的网络异常检测算法

网络异常检测模型可以用来检测未知攻击，具有良好的可扩展性，是目前入侵检测系统研究的热点。但目前的异常检测方法存在着误报率较高、检测效率不能满足高速网络实时检测需求等问题。本文通过对免疫智能算法与网络异常研究，提出了一种基于免疫智能的网络异常检测算法AIAIK。理论分析和实验说明改算法具有自然免疫系统的免疫网络、非线性、免疫记忆和克隆选择等良好特性，实验检测效果良好。

An Anomaly Detection Algorithm Based on Artificial Immune Intelligence

Network anomaly detection model can be used to detect unknown attacks, which has good scalability and it is the hot spot of intrusion detection system research. However, the existing anomaly detection methods have some problems such as high false positive rate and low detection efficiency, which can not meet the needs of high-speed network real-time detection. Combining artificial immune algorithm with network anomaly technology, an anomaly network detection algorithm based on artificial immune network (AIAIK) is proposed. Theoretical analysis and experimental results show that the algorithm has good characteristics such as immune network, nonlinear, immune memory and clonal selection of natural immune system, and the experimental detection effect is good.