Hybrid Geometrical and Statistical Models for Worm Propagation in Wireless Sensor Networks

Abstract

Worm propagation poses significant security threats to Wireless Sensor Networks (WSNs), leading to potential disruptions and system failures. Traditional modeling approaches, including geometrical and statistical models, offer insights into worm behavior but often lack the comprehensive capabilities required to fully understand and mitigate these threats. This article presents a novel hybrid geometrical and statistical model that integrates spatial dynamics with probabilistic infection analysis to provide a more accurate and robust framework for predicting and controlling worm propagation in WSNs. The hy brid model enhances predictive accuracy, informs effective mitigation strategies, and offers robust analytical tools for complex network in teractions. Detailed model formulation, analysis, and performance evaluation are provided, demonstrating the model’s applicability to various WSN scenarios.