Minimizing Energy Consumption for Intrusion Detection Model in Wireless Sensor Network

The security is one of the major concerns in today’s existing technology. Wireless Sensor Network (WSN) can be deployed in critical areas and network can be compromised by the malicious attack. Due to its unattended deployment strategy in remote places, security plays a major role and thus the primary line of defense is Intrusion detection system (IDS). The existing IDS cannot perform efficiently due to the mechanisms applied. Thus, a novel approach is designed and modeled to obtain high performance of WSN. In our proposed work, the probabilistic model which provides the direct way to visualize the model using joint probability, referred as Bayesian Network is combined with the stochastic process model called as Hidden Markov Model. This combined novel approach is a graphical model represented with nodes and edges. The evaluated results when obtained by applying the novel approach is observed and high detection rate is obtained when compared with the existing algorithms like weighted support vector machine (WSVM), K-means classifier and knowledge-based IDS (KBIDS). Maximum throughput and less transmission delay are obtained. The experiments are carried out for different attacks with various trained and test data. Thus, the novel approach gives overall high performance in WSN.