Graph based event measurement for analyzing distributed anomalies in sensor networks

Abstract

Wireless Sensor Network (WSN) has emerged drastically with numerous practical applications of considerable Engineering importance where privacy and security are of dominant influence. This paves the way for this investigation and present interest in the development of novel and innovative intrusion detection approach. This work anticipated a novel Intrusion detection framework by modeling sensor connectivity with a targeted graph and uses statistical graph properties by modeling intrusion detection. In anticipated graph-based detection, data capturing magnitude is modeled with the Gaussian model, and the corresponding correntropy is estimated by graph matrix with adaptive sensor measurements. Anticipated detection approach is modeled based on the Laplacian Matrix, and closed-form expressions are attained for statistical analysis. At last, temporal network analysis are characterized by evaluating sensor distance among measurement distributions in consecutive time. The results depict that the anticipated detection framework offers superior detection recital than compared to existing frameworks.