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Detecting malicious chaotic signals in wireless sensor network

Author

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  • Upadhyay, Ranjit Kumar
  • Kumari, Sangeeta

Abstract

In this paper, an e-epidemic Susceptible–Infected–Vaccinated (SIV) model has been proposed to analyze the effect of node immunization and worms attacking dynamics in wireless sensor network. A modified nonlinear incidence rate with cyrtoid type functional response has been considered using sleep and active mode approach. Detailed stability analysis and the sufficient criteria for the persistence of the model system have been established. We also established different types of bifurcation analysis for different equilibria at different critical points of the control parameters. We performed a detailed Hopf bifurcation analysis and determine the direction and stability of the bifurcating periodic solutions using center manifold theorem. Numerical simulations are carried out to confirm the theoretical results. The impact of the control parameters on the dynamics of the model system has been investigated and malicious chaotic signals are detected. Finally, we have analyzed the effect of time delay on the dynamics of the model system.

Suggested Citation

  • Upadhyay, Ranjit Kumar & Kumari, Sangeeta, 2018. "Detecting malicious chaotic signals in wireless sensor network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1129-1152.
    • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:1129-1152
    DOI: 10.1016/j.physa.2017.11.043
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    Citations

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    Cited by:

    1. Kumari, Sangeeta & Upadhyay, Ranjit Kumar, 2021. "Exploring the behavior of malware propagation on mobile wireless sensor networks: Stability and control analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 246-269.
    2. Das, Parthasakha & Das, Pritha & Mukherjee, Sayan, 2020. "Stochastic dynamics of Michaelis–Menten kinetics based tumor-immune interactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).

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