首页    期刊浏览 2024年09月16日 星期一
登录注册

文章基本信息

  • 标题:Intrusion Detection Techniques in Wireless Sensor Network using Data Mining Algorithms: Comparative Evaluation Based on Attacks Detection
  • 本地全文:下载
  • 作者:YOUSEF EL MOURABIT ; AHMED TOUMANARI ; ANOUAR BOUIRDEN
  • 期刊名称:International Journal of Advanced Computer Science and Applications(IJACSA)
  • 印刷版ISSN:2158-107X
  • 电子版ISSN:2156-5570
  • 出版年度:2015
  • 卷号:6
  • 期号:9
  • DOI:10.14569/IJACSA.2015.060922
  • 出版社:Science and Information Society (SAI)
  • 摘要:Wireless sensor network (WSN) consists of sensor nodes. Deployed in the open area, and characterized by constrained resources, WSN suffers from several attacks, intrusion and security vulnerabilities. Intrusion detection system (IDS) is one of the essential security mechanism against attacks in WSN. In this paper we present a comparative evaluation of the most performant detection techniques in IDS for WSNs, the analyzes and comparisons of the approaches are represented technically, followed by a brief. Attacks in WSN also are presented and classified into several criteria. To implement and measure the performance of detection techniques we prepare our dataset, based on KDD'99, into five step, after normalizing our dataset, we determined normal class and 4 types of attacks, and used the most relevant attributes for the classification process. We propose applying CfsSubsetEval with BestFirst approach as an attribute selection algorithm for removing the redundant attributes. The experimental results show that the random forest methods provide high detection rate and reduce false alarm rate. Finally, a set of principles is concluded, which have to be satisfied in future research for implementing IDS in WSNs. To help researchers in the selection of IDS for WSNs, several recommendations are provided with future directions for this research.
  • 关键词:thesai; IJACSA; thesai.org; journal; IJACSA papers; Keyword: Wireless sensor network; Anomaly Detection; Intrusion detection system; classification; KDD’99; Weka
国家哲学社会科学文献中心版权所有