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基于QPSO算法的异常检测方法

Anomaly detection approach based on quantum-behaved partical swarm optimization
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摘要 在入侵检测系统中,将正常状态与异常状态区分开来,是目前所面临的一个难点。针对这一问题,提出了在异常检测中运用量子粒子群算法(QPSO)对隶属度函数参数进行优化的方法。把隶属度函数里的参数组合当作一个粒子,在粒子的迭代进化中运用模糊数据挖掘的技术,可以搜索到最佳的参数组合,最大限度地把正常状态和异常状态区分开来,提高了异常检测的准确性,并通过实验验证了这一方法的可行性。 Differentiating the normal state and anomaly state is a difficult task in intrusion detection system,to solve the problem,an approach that applies Quantum-Behaved Partical Swarm Optimization（QPSO） to optimize parameters of membership functions in anomaly detection is presented.Parameters of membership functions are arranged into partical swarm,an optimal parameter-set could be derived by embedding fuzzy data mining in the process of evolution of partical,so normal state and anomaly state could be differentiated in the most extent,and the accuracy of anomaly detection is enhanced.Experiments prove the feasibility of the approach.
作者 张海芹 孙俊 须文波
机构地区 江南大学信息工程学院
出处 《计算机工程与应用》 CSCD 北大核心 2007年第8期129-130,148,共3页 Computer Engineering and Applications
基金 国家自然科学基金(the National Natural Science Foundation of China under Grant No.60474030) 。
关键词 异常检测 模糊数据挖掘 量子粒子群 anomaly detection fuzzy data mining Quantum Partical Swarm
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
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参考文献6

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二级参考文献9

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共引文献6

计算机工程与应用
2007年 第8期

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