基于改进多目标遗传算法的入侵检测集成方法

针对现有入侵检测算法中存在着对不同类型攻击检测的不均衡性以及冗余或无用特征导致的检测模型复杂与检测精度下降的问题,提出了一种基于改进多目标遗传算法的入侵检测集成方法.利用改进的多目标遗传算法生成检测率与误报率均衡优化的最优特征子集的集合,并采用选择性集成方法挑选精确的、具有多样性的基分类器构造集成入侵检测模型.实验结果表明,该算法能够有效地解决入侵检测中存在的特征选择问题,并在保证较高检测精度的基础上,对不同类型的攻击检测具有良好的均衡性.

An Ensemble Approach to Intrusion Detection Based on Improved Multi-Objective Genetic Algorithm

There exist some issues in current intrusion detection algorithms such as unbalanced detection performance on different types of attacks, and redundant or useless features that will lead to the complexity of detection model and degradation of detection accuracy. This paper presents an ensemble approach to intrusion detection based on improved multi-objective genetic algorithm. The algorithm generates the optimal feature subsets, which achieve the best trade-off between detection rate and false positive rate through an improved MOGA. And the most accurate and diverse base classifiers are selected to constitute the ensemble intrusion detection model by selective ensemble approach. The experimental results show that the algorithm can solve the feature selection problem of intrusion detection effectively. It can also achieve balanced detection performance on different types of attacks while maintaining high detection accuracy.