Comprehensive analysis of a stochastic wireless sensor network motivated by Black-Karasinski process

Area of Science:

• Computer Science
• Network Security
• Mathematical Modeling

Background:

• Wireless sensor networks (WSNs) face significant security vulnerabilities due to malware, particularly worm propagation.
• Infected nodes can rapidly spread worms to adjacent nodes, compromising network integrity.

Purpose of the Study:

• To introduce a novel stochastic model for characterizing worm spread dynamics in WSNs.
• To analyze the conditions for worm persistence or extinction within WSNs.

Main Methods:

• Developed a stochastic $$ model to represent worm transmission in WSNs.
• Established the existence of a globally positive solution for the model.
• Determined a threshold value and derived sufficient conditions for worm spread persistence/extinction based on mean behavior.

Main Results:

• The stochastic model demonstrates that environmental randomness can inhibit malware spread in WSNs.
• Approximate solutions derived using various parameter sets validate the model's findings.
• The proposed $$ model shows superior performance in mitigating worm transmission compared to existing models.

Conclusions:

• Environmental randomness is a key factor in controlling malware propagation in WSNs.
• The developed stochastic model provides a robust framework for analyzing and mitigating worm threats in WSNs.