基于改进FastICA算法的入侵检测样本数据优化方法
发布时间：2018-11-18 14:31
【摘要】：为更好实现对入侵检测样本数据的优化处理,提出了一种改进的快速独立成分分析(Fast ICA)算法,采用基于加权相关系数进行白化处理以减少信息损失,并优化牛顿迭代法使其满足三阶收敛。对算法进行了细致描述,分析了算法的时间复杂度。实验结果表明,该方法可有效减少数据信息损失,具有迭代次数少、收敛速度快等优点,可有效提高入侵检测样本数据的优化效率。
[Abstract]:In order to achieve the optimal processing of intrusion detection sample data, an improved fast independent component analysis (Fast ICA) algorithm is proposed, which is whitened based on weighted correlation coefficient to reduce information loss. The Newton iteration method is optimized to satisfy the third order convergence. The algorithm is described in detail and its time complexity is analyzed. Experimental results show that this method can effectively reduce the loss of data information, has the advantages of less iteration times and faster convergence speed, and can effectively improve the optimization efficiency of intrusion detection sample data.
【作者单位】： 北京交通大学计算机与信息技术学院;
【基金】：北京高校青年英才计划基金资助项目(No.YETP0548) 中央高校基本科研业务费基金资助项目(No.2014JBM030) 国家自然科学基金资助项目(No.61102105)~~
【分类号】：TP393.08
本文编号：2340299
[Abstract]:In order to achieve the optimal processing of intrusion detection sample data, an improved fast independent component analysis (Fast ICA) algorithm is proposed, which is whitened based on weighted correlation coefficient to reduce information loss. The Newton iteration method is optimized to satisfy the third order convergence. The algorithm is described in detail and its time complexity is analyzed. Experimental results show that this method can effectively reduce the loss of data information, has the advantages of less iteration times and faster convergence speed, and can effectively improve the optimization efficiency of intrusion detection sample data.
【作者单位】： 北京交通大学计算机与信息技术学院;
【基金】：北京高校青年英才计划基金资助项目(No.YETP0548) 中央高校基本科研业务费基金资助项目(No.2014JBM030) 国家自然科学基金资助项目(No.61102105)~~
【分类号】：TP393.08
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，本文编号：2340299
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