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| 基于改进多目标遗传算法的入侵检测集成方法 |
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俞研1,2, 黄皓1,2
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1.南京大学,计算机科学与技术系,江苏,南京,210093;2.计算机软件新技术国家重点实验室(南京大学),江苏,南京,210093
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| 摘要: |
| 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. |
| 关键词: 入侵检测 特征选择 优化 多目标遗传算法 选择性集成 |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant No.60303023 (国家自然科学基金); the National High-Tech Research and Development Plan of China under Grant No.2003AA142010 (国家高技术研究发展计划(863)); the High-Tech Research Plan of Jiangsu Province of China under Grant No.BG2004030 (江苏省高技术计划) |
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| An Ensemble Approach to Intrusion Detection Based on Improved Multi-Objective Genetic Algorithm |
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YU Yan,HUANG Hao
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| Abstract: |
| 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. |
| Key words: intrusion detection feature selection optimization multi-objective genetic algorithm selective ensemble |
