Abstract: This study proposes a distributed intrusion detection system for adhoc wireless networks using self organizing maps and mobile agents. In this research, we efficiently use log file data obtained from the local host for training the neural network, to analyze the adhoc wireless network for detecting intrusions. Security agents are used to monitor multiple clients of the wireless network to determine the correlation among the observed anomalous patterns and to report such abnormal behavior to the administrator and the user in order to take possible actions. From the system developed in this research, we obtained high intrusion-detection rates (99.2%) and low false-alarm rates. The main contribution of this paper is the provision of an agent based framework that is capable of detecting intruders and to forecast the anomalies using the neural classifier, self organizing maps.
S. Bose , P. Yogesh and A. Kannan , 2006. Neural Network Approach for Anomaly Intrusion Detection in Adhoc Networks Using Agents. International Journal of Soft Computing, 1: 108-110.