Abstract
In Wireless Sensor Networks (WSN), the intrusion detection technique may result in increased computational cost, packet loss, performance degradation and so on. In order to overcome these issues, in this study, we propose a fuzzy based anomaly intrusion detection system for clustered WSN. Initially the cluster heads are selected based on the parameters such as link quality, residual energy and coverage. Then the anomaly intrusion is detected using fuzzy logic technique. This technique considers the parameters such as honest, energy level, unselfishness and prediction variance of each cluster member and provides the optimal trust threshold of the node as the result. By simulation result, we show that the proposed technique enhances the detection accuracy and reduces the false positive rate.
Highlights
Wireless Sensor Networks (WSN): Wireless Sensor Networks (WSN) generally composed of a collection of self-organizing, low-power, low-cost, autonomous tiny devices called wireless sensor nodes spatially distributed by sensors to monitor and affect the environmental conditions like temperature, sound, vibration, pressure, motion or pollutants, at different locations
The cluster heads are selected based on the parameters such as link quality, residual energy and Unselfishness (Uij): This metric offer the degree of unselfishness of Nj as estimated by Ni based on direct observations over (0, t)
We can conclude that the delay of our proposed FBAIDS approach has 24% of less than Hierarchical Trust Management (HTM) approach
Summary
Wireless Sensor Networks (WSN): Wireless Sensor Networks (WSN) generally composed of a collection of self-organizing, low-power, low-cost, autonomous tiny devices called wireless sensor nodes spatially distributed by sensors to monitor and affect the environmental conditions like temperature, sound, vibration, pressure, motion or pollutants, at different locations. The nodal architecture is different for applications and designed for data collection, data management, data transfer and power supply. It has a wide area of applications like potential applications including burglar alarms, medical monitoring and emergency response, monitoring remote, target tracking in battlefields, disaster relief networks, military, early fire detection in forests and environmental monitoring (Zhang, 2009; Tiwari et al, 2009; Livani and Abadi, 2010, 2011; Hsieh et al, 2011; Coppolino et al, 2013; Bhuse and Gupta, 2006). The nodes are often placed in a hostile or dangerous or unreachable environment without physical protection (Zhang, 2009; Livani and Abadi, 2010, 2011; Hsieh et al, 2011; Coppolino et al, 2013; Li, 2010)
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