A Study on Identity Based Attack Detection and Localization by the Clustering in Wireless Sensor Network - Das, Tuhin (International Journal of Computer Sciences and Engineering, 2025)
Wireless sensor networks are most vulnerable to identity based attacks in which malicious device is used to create forged MAC addresses of specified authorized client. The identity of a node can be easily verified through cryptographic authentication techniques which are not always possible because it requires key management and other additional infrastructural overhead. In this paper we propose a system for detecting identity based attacks and also searching the actual positions of adversaries which are responsible for the attacks. Firstly we propose OADL (Attack Detection & Localization) model for identity based attack that utilizes correlation of nodes signal's spatial property (i.e spatial information, a physical property of each node) and the average received signal gain of received signal strength (RSS) collected from each wireless sensor nodes. Then we describe the integration of our attack detection model into our real time localization system, which has the ability to locate the actual positions of the attackers through Partitioning AroundMedoids clustering analysis for localization. We are able to show that the actual positions of the attackers that can be located using localization algorithms. Then we make evaluation based on our model through experimentation using both 802.11 network and 802.15.4 network model. Our results will indicate that identity based attack detection can be achieved with high precision in attack detection rate and localization of multiple adversaries.