Improving the performance of self-organizing maps for intrusion detection
Improving the performance of self-organizing maps for intrusion detection
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DOI：
10.1109/secon.2016.7506766
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发表时间：
2016-03
期刊：
影响因子：
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通讯作者：
Steven McElwee;J. Cannady
中科院分区：
文献类型：
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作者：
Steven McElwee;J. Cannady
自组织映射在入侵检测中的使用对于攻击分析是不实际的，这是由于大量数据所需的计算处理时间。虽然以前的研究已经解决了这个问题，通过优化算法用于自组织地图，并通过特征减少，有没有现有的解决方案，使用自组织地图的入侵检测，充分解决了计算性能的问题，使自组织地图实用的入侵检测数据的分析。该研究展示了一种预处理方法，包括离散化，重复数据删除，不平衡数据集的二进制过滤，以及特征提取，以提高自组织映射中聚类的性能和优化质量。
The use of self-organizing maps in intrusion detection has not been practical for attack analysis as a result of the computational processing time required for large volumes of data. Although previous research has addressed this problem through optimizing the algorithms used for self-organizing maps and through feature reduction, there is no existing solution for using self-organizing maps for intrusion detection that adequately addresses the problem of computational performance to make self-organizing maps practical for analysis of intrusion detection data. This research demonstrates a method of preprocessing that includes discretization, deduplication, binary filtering for imbalanced datasets, and feature extraction to improve the performance and optimize the quality of clustering in self-organizing maps.