基于遗传支持向量机模型的优化算法

Lanying Li, Shaobin Ma, Yun Zhang
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引用次数: 5

摘要

针对入侵检测系统准确率低的问题,结合遗传算法和支持向量机算法的特点,建立了遗传支持向量机模型。该模型首先根据遗传算法优化支持向量参数,然后利用优化后的支持向量机构建入侵检测模型,并利用该模型进行检测。实验通过讨论支持向量机参数对检测精度的影响,选择合适的参数c, s。结果表明,将遗传支持向量机模型应用于入侵检测,提高了检测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization Algorithm Based on Genetic Support Vector Machine Model
Aiming at the problem of low accuracy in intrusion detection system, this paper established a genetic support vector machine (SVM) model according to the features of genetic algorithm and support vector machine algorithm. The model firstly optimizes the support vector parameters according to genetic algorithm, then we build the intrusion detection model with support vector machine optimized and use the model to detect. The experiments choose the proper parameters (c, s) through discussing the influence of support vector machines parameters to the detection accuracy. The results show that putting genetic support vector machine model into intrusion detection improved detection accuracy.
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