Advertisement

A SEIS Model for Propagation of Random Jamming Attacks in Wireless Sensor Networks

  • Miguel López
  • Alberto PeinadoEmail author
  • Andrés Ortiz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 527)

Abstract

This paper describes the utilization of epidemiological models, usually employed for malware propagation, to study the effects of random jamming attacks, which can affect the physical and MAC/link layers of all nodes in a wireless sensor network, regardless of the complexity and computing power of the devices. The random jamming term considers both the more classical approach of interfering signals, focusing on the physical level of the systems, and the cybersecurity approach that includes the attacks generated in upper layers, mainly in the MAC/link layer, producing the same effect on the communication channel. We propose, as a preliminary modelling task, the epidemiological mathematical model Susceptible–Exposed–Infected–Susceptible (SEIS), and analyze the basic reproductive number, the infection rate, the average incubation time and the average infection time.

Keywords

Cyber security Jamming attacks Epidemiological models Wireless sensor networks 

Notes

Acknowledgements

This work has been supported by the project “Protocolos criptográficos para la ciberseguridad: identificación, autenticación y protección de la información (ProCriCiS)” (TIN2014-55325-C2-1-R) of the Ministry of Economy and Competitiveness and FEDER funds.

References

  1. 1.
    Kermack, W.O., McKendrick, A.G.: Contributions to the mathematical theory of epidemics, part I. Proc. Roy Soc Edin A 115, 700–721 (1927)CrossRefGoogle Scholar
  2. 2.
    Kephart, J.O., White, S.R.: Directed-graph epidemiological models of computer viruses. In: Proceedings of IEEE Symposium on Security and Privacy, pp. 343–359 (1991)Google Scholar
  3. 3.
    Azni, A.H., Ahmad, R., Mohamad Noh, Z.A.: Correlated node behavior in wireless ad hoc networks: an epidemic model. In: Proceedings of 7th International Conference for Internet Technology and Secured Transactions, pp. 403–410 (2012)Google Scholar
  4. 4.
    De, P., Liu, Y., Das, S.K.: Deployment-aware modeling of node compromise spread in wireless sensor networks using epidemic theory. ACM Trans. Sensor Networks 3, 1–33 (2009)CrossRefGoogle Scholar
  5. 5.
    Keshri, N., Mishra, B.K.: Optimal control model for attack of worms in wireless sensor network. Int. J. Grid Distrib. Comput. 7, 251–272 (2014)CrossRefGoogle Scholar
  6. 6.
    Mishra, B.K., Keshri, N.: Mathematical model on the transmission of worms in wireless sensor network. Appl. Math. Model. 37, 4103–4111 (2013)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Mishra, B.K., Srivastava, S.K.: A quarantine model on the spreading behavior of worms in wireless sensor network. Trans. IoT Cloud Comput. 2, 1–12 (2014)CrossRefGoogle Scholar
  8. 8.
    Tang, S.: A modified epidemic model for virus spread control in wireless sensor networks. Int. J. Wirel. Inf. Networks 18, 319–326 (2011)CrossRefGoogle Scholar
  9. 9.
    Xiaoming, W., Yingshu, L.: An improved SIR model for analyzing the dynamics of worm propagation in wireless sensor networks. Chin. J. Electron. 18, 8–12 (2009)Google Scholar
  10. 10.
    Martín del Rey, A.: Mathematical modeling of the propagation of malware: a review. Secur. Commun. Networks 8(15), 2561–2579 (2015)CrossRefGoogle Scholar
  11. 11.
    Chowdhury, M., Kader, M.F.: Asaduzzaman: security issues in wireless sensor networks: a survey. Int. J. Future Gener. Commun. Networking 6, 97–116 (2013)CrossRefGoogle Scholar
  12. 12.
    Francillon, A., Castelluccia, C.: Code injection attacks on harvard-architecture devices. In: Proceedings of the 15th ACM conference on Computer and communications security, pp. 15–26. ACM (2008)Google Scholar
  13. 13.
    Habibi, J., Gupta, A., Carlsony, S., Panicker, A., Bertino, E.: Mavr: code reuse stealthy attacks and mitigation on unmanned aerial vehicles. In: Distributed Computing Systems (ICDCS), pp. 642–652. IEEE (2015)Google Scholar
  14. 14.
    Braden, K., Crane, S., Davi, L., Franz, M., Larsen, P., Liebchen, C., Sadeghi, A.R.: Leakage-resilient layout randomization for mobile devices. In: Network and Distributed Systems Security Symposium (NDSS) (2016)Google Scholar
  15. 15.
    Modares, H., Moravejosharieh, A., Salleh, R., Lloret, J.: Security overview of wireless sensor network. Life Sci. J. 10, 1627–1632 (2013)Google Scholar
  16. 16.
    Znaidi, W., Minier, M., Babau, J.P.: An ontology for attacks in wireless sensor networks. Unité de recherche INRIA Rhône-Alpes, Rapport de recherche Nº 6704, pp 1–13 (2008)Google Scholar
  17. 17.
    Sokullu, R., Korkmazy, I., Dagdevirenz, O., Mitsevax, A., Prasad, N.R.: An investigation on IEEE 802. 15. 4 MAC layer attacks. In: Proceedings of The 10th International Symposium on Wireless Personal Multimedia Communications (WPMC) (2007)Google Scholar
  18. 18.
    Guglielmo, D., Brienza, S., Anastasi, G.: IEEE 802.15.4e: A survey. Comput. Commun. 88, 1–24 (2016)CrossRefGoogle Scholar
  19. 19.
    Hethecote, H.W.: The mathematics of infectious diseases. SIAM Rev. 42, 599–653 (2000)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Brauer, F., van den Driessche, P., Wu, J.: Mathematical Epidemiology. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  21. 21.
    Lichtman, M., Poston, J.D., Amuru, S., Shahriar, C., Clancy, T.C., Buehrer, R.M., Reed, J.H.: A communications jamming taxonomy. IEEE Secur. Priv. 14, 47–54 (2016)CrossRefGoogle Scholar
  22. 22.
    Wei, Y., van Hoesel, L.L., Doumen, J., Hartel, P., Havinga, P.: Energy efficient link layer jamming attacks against wireless sensor network MAC protocols. In: SANS 2005 (2005)Google Scholar
  23. 23.
    Mohammadi, S., Jadidoleslamy, V.: A comparison of link layer attacks on wireless sensor networks. Int. J. Appl. Graph Theory Wireless ad hoc Networks Sensor Networks 3, 35–56 (2011)CrossRefGoogle Scholar
  24. 24.
    Zhu, L., Zhao, H.: Dynamical analysis and optimal control for a malware propagation model in an information network. Neurocomputing 149, 1370–1386 (2015)CrossRefGoogle Scholar
  25. 25.
    Hirsch, M.W., Smale, S., Devaney, R.L.: Differential equations, dynamical systems, and an introduction to chaos. In: 3rd Ed., Academic Press (2012)Google Scholar
  26. 26.
    Perko, L.: Differential Equations and Dynamical Systems, 3rd edn. Springer, Heidelberg (2010)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  1. 1.E.T.S.Ingeniería de Telecomunicación, Dept. Ingeniería de ComunicacionesUniversidad de Málaga, Andalucía TechMálagaSpain

Personalised recommendations