一种改进的动态克隆选择算法在入侵检测中的应用

Research of the Intrusion Detection Model Based on Extended Dynamic Clonal Selection

针对目前入侵检测系统不能有效检测已知攻击的变种和未知攻击行为的缺陷,受免疫系统中动态克隆选择算法的启发,提出了一种基于改进的动态克隆选择算法。该算法可以适应连续改变的环境,动态地学习变化的"正常"模式以及预测新的"异常"模式。经实验证明,该算法在入侵检测中,在降低误报率的情况下,提高了检测率。

Because intrusion detection systems couldn't detect the mutant of existing intrusion behavior and undefined intrusion behavior effectively,according to the Dynamic Clonal Selection algorithms in the biological immune system,this paper presents an intrusion detection model based on extended Dynamic Clonal Selection algorithms.This algorithms adapt to continuously changing environments,dynamically learning the fluid normal patterns and predict new anomaly patterns.Experiment results show that this algorithms improves the detection rate and maintains a low false alarm rate,In aspects of the intrusion detection.