Intrusion aware data transmission framework in wireless sensor network

Abstract

Intrusion attack is considered as the major concerns to be focussed in wireless sensor network which should be seriously viewed for identification of secure and trustworthy information processing. The various characteristics involved in Intrusion attacks should be adapted precisely since it impacts on result of the intrusion detection in terms of accuracy. PCA-based centralized approach (PCACID) and Knowledge based Intrusion Detection Strategy (KBIDS) is suggested in this research for achieving the accurateintrusion detection. Though KBIDS is involved in achieving accurate detection, the demerit is that time complexity and computational overhead are progressively more which in turn influences on the entire network performance. Traffic Variation based Intrusion Detection System (TV-IDS) plays a major role in mitigating these issues. In addition to it, Fuzzy based mean shift clustering is also suggested for incorporating clustering feature process which influences precise clustering result with the advantage of less time complexity. The decision classifier takes its role after the assessment of data points bias variations. This variation factor helps in recognizing smaller traffic variation and not determined as irregular data. The classification is achieved by hybrid genetic neuro fuzzy classifier. The updating of ANFIS weight values is accomplished concurrently with optimal selection by means of genetic algorithm. The optimal route path is chosen by greatly utilizing the artificial bee colony algorithm. The various fitness parameters involved in this research are energy level of nodes, bandwidth, etc., for efficient data transmission successfully. MATLAB simulation platform is greatly utilized for assessment of overall results for validating that proposed TV-IDS achieves improved outcomes comparatively.