期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2011
卷号：2
期号：4
页码：1774-1779
出版社：TechScience Publications
摘要：MANET has no clear line of defense so, it is accessible to both legitimate network nodes and malicious nodes. Some of the nodes may be selfish, for example, by not forwarding the packets to the destination, thereby saving the battery power. Some others may act malicious by launching security attacks like black hole or hack the information. Traditional way of protecting networks with firewalls and encryption software is no longer sufficient. Therefore, intrusion detection system is required that monitor the network, detect malicious node and notifies other node in the network to avoid malicious node i. e. IDS detects malicious activities in the networks. We have implemented k-means clustering algorithm of data mining for efficient detection of intrusions in the MANET traffic and also generated black hole attacks in the network. In data mining, clustering is the most important unsupervised learning process used to find the structures or patterns in a collection of unlabeled data. We have used the K-means algorithm to cluster and analyze the data in this paper. The simulation of the proposed method is performed in NS2 simulator and we got the result as we expected.
关键词：MANET; cluster; Intrusion Detection System; kmeans;clustering algorithm; data mining; black hole attack.ces.
