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.

无线传感器网络（WSN）的出现已经引起了工程学上的大量重视，其中隐私和安全性是主要影响力的大量实际应用。这为这项研究铺平了道路，并引起了人们对开发新颖和创新的入侵检测方法的兴趣。这项工作通过对目标图与传感器的连通性进行建模，从而期待了一种新颖的入侵检测框架，并通过对入侵检测进行建模来使用统计图属性。在预期的基于图的检测中，数据捕获幅度使用高斯模型建模，并通过具有自适应传感器测量值的图矩阵来估计相应的熵。基于拉普拉斯矩阵对预期的检测方法进行建模，并获得封闭形式的表达式以进行统计分析。最后，时态网络分析的特征是在连续时间内评估测量分布之间的传感器距离。结果表明，与现有框架相比，预期的检测框架具有更好的检测独奏性。