Hybrid Weighted K-Means Clustering and Artificial Neural Network for an Anomaly-Based Network Intrusion Detection System

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Systems Pub Date : 2018-03-28 DOI:10.1515/jisys-2016-0105
Rafath Samrin, Vasumathi Devara
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引用次数: 11

Abstract

Abstract Despite the rapid developments in data technology, intruders are among the most revealed threats to security. Network intrusion detection systems are now a typical constituent of network security structures. In this paper, we present a combined weighted K-means clustering algorithm with artificial neural network (WKMC+ANN)-based intrusion identification scheme. This paper comprises two modules: clustering and intrusion detection. The input dataset is gathered into clusters with the usage of WKMC in clustering module. In the intrusion detection module, the clustered information is trained with the utilization of ANN and its structure is stored. In the testing process, the data are tested by choosing the most suitable ANN classifier, which corresponds to the closest cluster to the test data, according to distance or similarity measures. For experimental evaluation, we used the benchmark database, and the results clearly demonstrated that the proposed technique outperformed the existing technique by having better accuracy.
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基于异常的网络入侵检测系统的混合加权k均值聚类与人工神经网络
摘要尽管数据技术发展迅速,但入侵者是最容易暴露的安全威胁之一。网络入侵检测系统是网络安全结构的一个典型组成部分。提出了一种加权k均值聚类算法与人工神经网络(WKMC+ANN)相结合的入侵识别方案。本文包括两个模块:聚类和入侵检测。在聚类模块中使用WKMC对输入数据集进行聚类。在入侵检测模块中,利用人工神经网络对聚类信息进行训练并存储其结构。在测试过程中,根据距离或相似度度量,选择最适合的ANN分类器,该分类器对应于与测试数据最近的聚类。对于实验评估,我们使用基准数据库,结果清楚地表明,所提出的技术优于现有技术,具有更好的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Intelligent Systems
Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
3.30%
发文量
77
审稿时长
51 weeks
期刊介绍: The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.
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