文章基本信息

标题：Cluster Based Anomaly Detection in Wireless LAN
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作者：P.Kavitha ; M.Usha
期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2014
卷号：12
期号：5
页码：227-230
DOI：10.14445/22312803/IJCTT-V12P146
出版社：Seventh Sense Research Group
摘要：Data mining methods have gained importance in addressing computer network security. Existing Rule based classification models for anomaly detection are ineffective in dealing with dynamic changes in intrusion patterns and characteristic. Unsupervised learning methods have been given a closer look for network anomaly detection. We investigate hierarchical clustering algorithm for anomaly detection in wireless LAN traffic. Since there is no standard datasets available to do research in wireless network, we simulated a wireless LAN using NS2 and the traces are used to observe the traffic patterns. Our study demonstrates the usefulness and promise of the proposed approach which uses hierarchical cluster based framework for anomaly detection in wireless computer networks to produce low false positive alarm and high detection rate also compared with the real time wireless traffic. This system can help Wireless network management system to quickly identify the attacks, which extends the system administrators security management capabilities and improve the integrity of the information security infrastructures.
关键词：Anomaly detection; Wireless Network; Data mining; Clustering ; Wireless LAN Traffic data.