Skip to main content

Advertisement

Springer Nature Link
Log in

Influence of Clamor on the Transmission of Worms in Remote Sensor Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Wireless Sensor Network (WSN) with remote sensing capability is gaining popularity in many of the real time applications such as military, healthcare, environment, home and other commercial applications.WSN is typically composed of various components such as sensor node, relay node, cluster head, gateway, base station. Such a critical network is vulnerable to most dangerous threats caused by worms towards the integrity and confidentiality of information passed through it. The study of the influence of clamor in propagation of potential of worm in WSN is of more significance. In this paper, a logical model is proposed that is reliant on pandemic theory. It is an improvement of the SIRS, SEIS and models. We propose an altered SEIRS (Susceptible-Exposed-Infectious-Recovered-Susceptible) model with the added substance background noise that overcomes the drawbacks of the existing models. The close by adequacy of the model has been affirmed using Lyapunov’s work. We similarly address the effect of node fluctuations in the model through numerical simulations that is carried out to prove that our proposed system is mean square stable and resistance against fluctuations with respect to the spread of worms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Geetha, R., Suntheya, A. K., & Umarani, G. (2020). Cloud integrated IoT enabled sensor network security: research issues and solutions. Wireless Personal Communications, 113, 747–771.

    Article  Google Scholar 

  2. Hu, F., Li, S., Xue, T., & Li, G. (2012). Design and analysis of low-power body area networks based on biomedical signals. International Journal of Electcs, 99(6), 811–822.

    Article  Google Scholar 

  3. Geetha, R., Madhusudhanan, V., Padmavathy, T., & Lallithasree, A. (2019). A light weight secure communication scheme for wireless sensor networks. Wireless Personal Communications, 108, 1957–1976.

    Article  Google Scholar 

  4. Kumar, V., Dhok, S. B., Tripathi, R., & Tiwari, S. (2017). Cluster size optimization with Tunable Elfes sensing model for single and multi-hop wireless sensor networks. International Journal of Electronics, 104(2), 312–327.

    Article  Google Scholar 

  5. LaSalle, J. P. (1976). The stability of dynamical systems, CBMS-NSF Reg. In Conference series in applied mathematics, SIAM, Philadelphia.

  6. Mishra, B. K., & Saini, D. (2007). Mathematical models on computer viruses. Applied Mathematics and Computation, 187(2), 929–936.

    Article  MathSciNet  Google Scholar 

  7. Zheng, H., Li, D., & Gao, Z. (2006, August). An epidemic model of mobile phone virus. In 2006 first international symposium on pervasive computing and applications (pp. 1–5). IEEE.

  8. MadhuSudanan, V., & Geetha, R. (2020). Dynamics of epidemic computer virus spreading model with delays. Wireless Personal Communications, 115(3), 2047–2061.

    Article  Google Scholar 

  9. Van den Driessche, P., & Watmough, J. (2002). Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Mathematical Biosciences, 180(1–2), 29–48.

    Article  MathSciNet  Google Scholar 

  10. Zhang, T., Yang, L. X., Yang, X., Wu, Y., & Tang, Y. Y. (2017). Dynamic malware containment under an epidemic model with alert. Physica A: Statistical Mechanics and its Applications, 470, 249–260.

    Article  MathSciNet  Google Scholar 

  11. Mishra, B. K., & Jha, N. (2007). Fixed period of temporary immunity after run of anti-malicious software on computer nodes. Applied Mathematics and Computation, 190(2), 1207–1212.

    Article  Google Scholar 

  12. Mishra, B. K., Nayak, P. K., & Jha, N. (2009). Effect of quarantine nodes in SEQIAmS model for the transmission of malicious objects in computer network. International Journal of Mathematical Modeling, Simulation and Applications, 2(1), 102–113.

    Google Scholar 

  13. Byun, H., & So, J. (2015). Node scheduling control inspired by epidemic theory for data dissemination in wireless sensor-actuator networks with delay constraints. IEEE Transactions on Wireless Communications, 15(3), 1794–1807.

    Article  Google Scholar 

  14. Ojha, R. P., Srivastava, P. K., & Sanyal, G. (2019). Improving wireless sensor networks performance through epidemic model. International Journal of Electronics, 106(6), 862–879.

    Article  Google Scholar 

  15. Nwokoye, C., & Umeh, I. (2018). Analytic-agent cyber dynamical systems analysis and design method for modeling spatio-temporal factors of malware propagation in wireless sensor networks. MethodsX, 5, 1373–1398.

    Article  Google Scholar 

  16. De, P., Liu, Y., & Das, S. K. (2009). Deployment-aware modeling of node compromise spread in wireless sensor networks using epidemic theory. ACM Transactions on Sensor Networks (TOSN), 5(3), 1–33.

    Article  Google Scholar 

  17. De, P., Liu, Y., & Das, S. K. (2008). An epidemic theoretic framework for vulnerability analysis of broadcast protocols in wireless sensor networks. IEEE Transactions on Mobile Computing, 8(3), 413–425.

    Article  Google Scholar 

  18. Gelenbe, E., Kaptan, V., & Wang, Y. (2004). Biological metaphors for agent behavior. In International symposium on computer and information sciences (pp. 667–675). Springer, Heidelberg.

  19. Madar, N., Kalisky, T., Cohen, R., Ben-avraham, D., & Havlin, S. (2004). Immunization and epidemic dynamics in complex networks. The European Physical Journal B, 38(2), 269–276.

    Article  Google Scholar 

  20. Singh, A., Awasthi, A. K., Singh, K., & Srivastava, P. K. (2018). Modeling and analysis of worm propagation in wireless sensor networks. Wireless Personal Communications, 98(3), 2535–2551.

    Article  Google Scholar 

  21. Mishra, B. K., & Keshri, N. (2013). Mathematical model on the transmission of worms in wireless sensor network. Applied Mathematical Modelling, 37(6), 4103–4111.

    Article  MathSciNet  Google Scholar 

  22. Haghighi, M. S., Wen, S., Xiang, Y., Quinn, B., & Zhou, W. (2016). On the race of worms and patches: Modeling the spread of information in wireless sensor networks. IEEE Transactions on Information Forensics and Security, 11(12), 2854–2865.

    Article  Google Scholar 

  23. Ho, J. W., & Wright, M. (2017). Distributed detection of sensor worms using sequential analysis and remote software attestations. IEEE Access, 5, 680–695.

    Article  Google Scholar 

  24. Feng, L., Song, L., Zhao, Q., & Wang, H. (2015). Modeling and stability analysis of worm propagation in wireless sensor network. In Mathematical problems in engineering, 2015.

  25. Faghani, M. R., & Nguyen, U. T. (2013). A study of XSS worm propagation and detection mechanisms in online social networks. IEEE Transactions on Information Forensics and Security, 8(11), 1815–1826.

    Article  Google Scholar 

  26. Ojha, R. P., Srivastava, P. K., Awasthi, S., & Sanyal, G. (2017). Global stability of dynamic model for worm propagation in wireless sensor network. In Proceeding of international conference on intelligent communication, control and devices (pp. 695–704). Springer, Singapore.

  27. Awasthi, S., Kumar, N., & Srivastava, P. K. (2020). A study of epidemic approach for worm propagation in wireless sensor network. In Intelligent computing in engineering (pp. 315–326). Springer, Singapore.

  28. Srivastava, A. P., Awasthi, S., Ojha, R. P., Srivastava, P. K., & Katiyar, S. (2016). Stability analysis of SIDR model for worm propagation in wireless sensor network. Indian Journal of Science and Technology, 9(31), 1–5.

    Google Scholar 

  29. Upadhyay, R. K., Kumari, S., & Misra, A. K. (2017). Modeling the virus dynamics in computer network with SVEIR model and nonlinear incident rate. Journal of Applied Mathematics and Computing, 54(1–2), 485–509.

    Article  MathSciNet  Google Scholar 

  30. Upadhyay, R. K., & Kumari, S. (2018). Bifurcation analysis of an e-epidemic model in wireless sensor network. International Journal of Computer Mathematics, 95(9), 1775–1805.

    Article  MathSciNet  Google Scholar 

  31. Chien, E. (2005). Security response: symbos. Mabir: Symantec Corporation.

    Google Scholar 

  32. Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London: Series A, Containing papers of a mathematical and physical character, 115(772), 700–721.

    MATH  Google Scholar 

  33. López, M., Peinado, A., & Ortiz, A. (2016). A SEIS model for propagation of random jamming attacks in wireless sensor networks. In International joint conference SOCO’16-CISIS’16-ICEUTE’16 (pp. 668–677). Springer, Cham.

  34. Mishra, B. K., & Saini, D. K. (2007). SEIRS epidemic model with delay for transmission of malicious objects in computer network. Applied Mathematics and Computation, 188(2), 1476–1482.

    Article  MathSciNet  Google Scholar 

  35. Ojha, R. P., Sanyal, G., Srivastava, P. K., & Sharma, K. (2017). Design and analysis of modified SIQRS model for performance study of wireless sensor network. Scalable Computing: Practice and Experience, 18(3), 229–242.

    Google Scholar 

  36. Nisbet, R. M., & Gurney, W. (2003). Modelling fluctuating populations: reprint of first edition.

Download references

Funding

Not Applicable.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, S.A. Engineering College, Chennai, Tamil Nadu, 600077, India

    R. Geetha

  2. Department of Mathematics, S.A. Engineering College, Chennai, Tamil Nadu, 600077, India

    V. Madhusudanan

  3. Department of Mathematics, School of Advanced Sciences, VIT, Vellore, Tamil Nadu, 632014, India

    M. N. Srinivas

Authors
  1. R. Geetha
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. V. Madhusudanan
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. M. N. Srinivas
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to R. Geetha.

Ethics declarations

Conflict of interest

No conflict of interest.

Availability of Data and Material

Not Applicable.

Code Availability

Not Applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Geetha, R., Madhusudanan, V. & Srinivas, M.N. Influence of Clamor on the Transmission of Worms in Remote Sensor Network. Wireless Pers Commun 118, 461–473 (2021). https://doi.org/10.1007/s11277-020-08024-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-08024-4

Keywords