基于k-均值聚类三角支持向量机的入侵检测特征选择优化

R. Ashok, A. Lakshmi, G. D. V. Rani, Madarapu Naresh Kumar
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引用次数: 9

摘要

随着基于网络的应用的快速发展,攻击者的威胁和安全威胁呈指数级增长。在各种基于网络的环境中,数据的误导显示了许多经济损失。每天,在网络和计算机产品中都会发现新的漏洞,从而导致新的问题出现。入侵检测系统(IDS)是一种新的网络威胁防御技术。特征选择是入侵检测中最具挑战性的问题,它可以减少属性(例如KDD cup'99,一个入侵检测数据集)中无用和冗余的特征。本文通过评价特征与类之间的关系,利用信息度量(Information Measure, IM)来计算特征之间的距离关系,从而减少特征向量空间,增强特征的选择能力。本文将信息度量(IM)方法与基于k-均值聚类三角面积的支持向量机(CTSVM)和支持向量机(SVM)分类器相结合来检测入侵攻击。该方法同时处理连续和离散属性,提取出具有较高检测率(DR)和误报率(FPR)的特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimized feature selection with k-means clustered triangle SVM for Intrusion Detection
With the rapid progress in the network based applications, the threat of attackers and security threats has grown exponentially. Misleading of data shows many financial losses in all kind of network based environments. Day by day new vulnerabilities are detected in networking and computer products that lead to new emerging problems. One of the new prevention techniques for network threats is Intrusion Detection System (IDS). Feature selection is the major challenging issues in IDS in order to reduce the useless and redundant features among the attributes (e.g. attributes in KDD cup'99, an Intrusion Detection Data Set). In this paper, we aim to reduce feature vector space by calculating distance relation between features with Information Measure (IM) by evaluating the relation between feature and class to enhance the feature selection. Here we incorporate the Information Measure (IM) method with k-means Cluster Triangular Area Based Support Vector Machine (CTSVM) and SVM (Support Vector Machine) classifier to detect intrusion attacks. By dealing with both continuous and discrete attributes, our proposed method extracts best features with high Detection Rate (DR) and False Positive Rate (FPR).
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