Malware propagation in Wireless Sensor Networksglobal models vs Individual-based models

  1. MARTÍN DEL REY, Ángel 1
  2. BATISTA, F. K. 1
  3. QUEIRUGA DIOS, A. 1
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

Revista:
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

ISSN: 2255-2863

Año de publicación: 2017

Volumen: 6

Número: 3

Páginas: 5-15

Tipo: Artículo

DOI: 10.14201/ADCAIJ201763515 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

Objetivos de desarrollo sostenible

Resumen

The main goal of this work is to propose a new framework to design a novel family of mathematical models to simulate malware spreading in wireless sensor networks (WSNs). An analysis of the proposed models in the scientific literature reveals that the great majority are global models based on systems of ordinary differential equations such that they do not consider the individual characteristics of the sensors and their local interactions. This is a major drawback when WSNs are considered. Taking into account the main characteristics of WSNs (elements and topologies of network, life cycle of the nodes, etc.) it is shown that individual-based models are more suitable for this purpose than global ones. The main features of this new type of malware propagation models for WSNs are stated.

Referencias bibliográficas

  • Al-Fuqaha, A., and Benhaddou, D., 2015. Wireless Sensor and Mobile Ad-Hoc Networks: Vehicular and Space Applications. Springer, NY. Akyildiz, I.F., Melodia, T., and Chowdhury, K.R., 2007. A survey on wireless multimedia sensor networks. Computer Networks 51: 921-960.
  • Dorca Josa, A., Serra-Ruiz, J., 2014. Implementación de un ataque DoS a redes WPAN 802.15.4. Actas de la XIII Reunión Española sobre Criptología y Seguridad de la Información, Alicante, España, pp. 327-332.
  • Dos Santos, J., Hennebert C. and Lauradoux, C., 2015. Preserving privacy in secured ZigBee wireless sensor networks. Proceedings of the 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), Milan, pp. 715-720.
  • Fahmy, H.M.A., 2016. Wireless Sensor Networks. Concepts, Applications, Experimentation and Analysis. Springer, Singapore.
  • Ferrer, J., Prats, C., López, D. Valls, J., and Gargallo, D., 2010. Contribution of Individual-based Models in malaria elimination strategy design. Malaria Journal 9: P9.
  • Flores Carbajal E. E., 2012. Red de sensores inalámbricas aplicado a la medicina - Master’s thesis, Escuela Técnica Superior de Ingenieros Industriales y de Telecomunicación. Universidad de Cantabria, España.
  • Karyotis, V., and Khouzani, M.H.R., 2016. Malware Diffusion Models for Modern Complex Networks. Theory and Applications, Morgan Kaufmann, Cambridge, MA.
  • Kermack, W. O., and McKendrick, A. G., 1927. A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London A 115: 700-721.
  • Martín del Rey, A., 2015. Mathematical modeling of the propagation of malware: a review. Secure and Communication Networks 8(15): 2561-2579.
  • Mohammadi, S., Atani, R. and Jadidoleslamy, H., 2011. A Comparison of Link Layer Attacks on Wireless Sensor Networks. Jour-nal of Information Security 2(2): 69-84.
  • Oreku, G.S., and Pazynyuk, T., 2016. Security in Wireless Sensor Networks. Springer.
  • Peng, S., Yu, S., and Yang, A., 2014. Smartphone Malware and Its Propagation Modeling: A Survey. IEEE Communications Sur-veys & Tutorials 16(2): 925-941.
  • Queiruga-Dios, A., Hernández Encinas, A., and Martín-Vaquero, J., 2016. Malware Propagationn in Wireless Sensor Networks: A Review, in: E. Corchado et al. (Eds.), Advances in Intelligence Systems and Computing vol. 527, Springer, pp. 648-657.
  • Raghu Vamsi, P. and Kant, K., 2016. Detecting Sybil Attacks in Wireless Sensor Networks Using Sequential Analysis. International Journal on Smart Sensing and Intelligent Systems 9(2): 651-680.
  • Railsback, S.F., and Grimm, V., 2011. Agent-Based and Individual-Based Modeling: A Practical Introduction. Princeton University Press, Princeton, NJ.
  • Selmic, R. R., Phoha, V. V., and Serwadda, A., 2016. Wireless Sensor Networks - Security, Coverage, and Localization (1 ed.). Springer.
  • Smieszek, T., Balmer, M., Hattendorf, J., Axhausen, K.W., Zinsstag, J., and Scholz, R.W., 2011. Reconstruction the 2003/2004 H3N2 influenza epidemic in Switzerland with a spatially explicit, individual-ased model. BMC Infectious Diseases 11: 115.
  • Sun, L., Ma, H., Fang, D., Niu, J., and Wang, W. (Eds.), 2015. Advances in Wireless Sensor Networks vol. 501, Springer.
  • Uchmanski, K., and Grimm, V., 1996. Individual based modelling in ecology: what makes the difference? Trends in Ecology and Evolution 12: 112.
  • Wang, X., He, Z., Zhao, X., Lin, C., Pan, Y., and Cai, Z., 2013. Reaction-diffusion modeling of malware propagation in mobile wireless sensor networks. Science China Information Sciences 56(9): 1-18.
  • Wang, Y., Wen, S., Xiang, Y., and Zhou, W., 2014. Modeling the Propagation of Worms in Networks: A Survey. IEEE Communi-cations Surveys & Tutorials 16(2): 942-960.
  • Wolfram, S., 1992. A New Kinf od Science. Wolfram Media, Champaign, IL.
  • Yang, S.H., 2014. Wireless Sensor Networks. Principles, Design and Applications. Springer, London.
  • Zema, N.R., Natalizio, E., Poss, M., Ruggeri, G., Molinaro, A., 2014. Healing wireless sensor networks from malicious epidemic diffusion. Proceedings of the 2014 IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), pp. 171-178.
  • Zhao, F., and Guibas, L.J., 2004. Wireless Sensor Networks. An Information Processing Approach. Morgan Kaufmann, San Fran-cisco, CA.
  • Zu, L., and Zhao, H., 2015. Dynamical analysis and optimal control for malware propagation model in an information network. Neurocomputing 149: 1370-1386.
Fuente de los datos: Dialnet