A reliable, fast and accurate intrusion detection system for wireless sensor network

Wireless Sensor Network (WSN) being one of the most demanding and ascending research area remain mostly vulnerable to Denial of Service (DOS) and Distributed Denial of Service (DDoS). These attacks significantly affect the performance of the network and eventually lead to complete compromise of all sensor nodes of the network. Many researchers have studied the identification and prevention of intrusion activities. LEoNIDS architecture solved the energy-latency tradeoff by giving minimum power utilization as well as minimum detection latency all at once. However, LEoNIDS architecture focused on the latency but not the precise detection of intrusion. The present work resolved these issues by presenting the technique called Attack Feature based fast and Accurate Intrusion Detection System (AF-FAIDS). The presented method enabled the intrusion detection in an effective way with enhanced delay and latency parameter. By using machine learning methods like Weighted Gaussian RBF kernel based Support Vector Machine (WGRBFK based SVM), attack detection ratio was enhanced which learnt the attacks features in an effective way. The technique provided precise and quicker identification of intrusion attacks which decreased the latency.