TY  - JOUR
T1  - Neural Network Approach for Anomaly Intrusion Detection in Adhoc Networks Using Agents
AU - , S. Bose AU - , P. Yogesh AU - , A. Kannan 
JO  - International Journal of Soft Computing
VL  - 1
IS  - 2
SP  - 108
EP  - 110
PY  - 2006
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2006.108.110
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2006.108.110
KW  - Intrusion detection system
KW  -mobile agent
KW  -adhoc network
KW  -SOM
AB  - 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.
ER  -