Optimal Performance for Intrusion Detection in WirelessLan Network Using Data MiningTechniques

Abstract

The objective of this paper is to identify the intruder of the wireless local area network based on the network and transport layer while accessing the internet within organizations and industries. The Intrusion detection system is the security that attempts to identify anomalies attributes who are trying to misuse a network without authorization and those who have legitimate access to the system but are abusing their privileges. The fact of the existing system deals with a firewall to protect and detect the unauthorized person using Wireless Local Area Network. Since the administrator may block or unblock the intruder based on the priority. This paper presents an enhanced framework, to detect and monitor the anomalies in the wireless sensor networks in an organization or an institution. The proposed approach to detect and filter the intruder in the wireless local area networks. Hence optimize the intrusion detection system in the particular organization or industries. The proposed IDS results are compared with the existing Decision Tree, Naive Bayes, and Random Forest algorithms.