MODELLING MALWARE RESPONSE IN WIRELESS SENSOR NETWORKS USING STOCHASTIC CELLULAR AUTOMATA

Hassan Chizari, Ahmad Uways Zulkurnain

Abstract


A model based on cellular automata to analyze malware propagation is enhanced to simulate malware response using self-propagating software updates. The model maintains the characteristics of wireless sensor networks while adding states and behavior. The simulation tests variable update sources and variable distances between infection and update source. The simulation results confirm that node density influences the propagation speed. In addition, using more updater nodes greatly increases the speed of recovery and significantly reduces death rate. Finally, attacks occurring further away from updater nodes take longer to detect and cause a greater impact.


Keywords


Wireless Sensor Networks (WSN); Malware Propagation; Cellular Automata; Remote Code Update; Attack Response

Full Text:

PDF

References


Akyildiz, Ian F., et al. "Wireless sensor networks: a survey." Computer networks 38.4 (2002): 393-422.

Werner-Allen, Geoffrey, et al. "Monitoring volcanic eruptions with a wireless sensor network." Wireless Sensor Networks, 2005. Proceeedings of the Second European Workshop on. IEEE, 2005.

Mainwaring, Alan, et al. "Wireless sensor networks for habitat monitoring." Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications. ACM, 2002.

Lee, Sang Hyuk, et al. "Wireless sensor network design for tactical military applications: remote large-scale environments." Military Communications Conference, 2009. MILCOM 2009. IEEE. IEEE, 2009.

Baggio, Aline. "Wireless sensor networks in precision agriculture." ACM Workshop on Real-World Wireless Sensor Networks (REALWSN 2005), Stockholm, Sweden. 2005.

Lo, Benny, et al. "Body sensor network-a wireless sensor platform for pervasive healthcare monitoring." The 3rd International Conference on Pervasive Computing. Vol. 13. 2005.

Niu, Ruixin, and Pramod K. Varshney. "Distributed detection and fusion in a large wireless sensor network of random size." EURASIP Journal on Wireless Communications and Networking 2005.4 (2005): 462-472.

Nicol, David M. "The impact of stochastic variance on worm propagation and detection." Proceedings of the 4th ACM workshop on Recurring malcode. ACM, 2006.

M. Nekovee, "Worm epidemics in wireless ad hoc networks," New Journal of Physics, vol. 9, pp. 189, 2007.

S. A. Khayam and H. Radha, "Using signal processing techniques to model worm propagation over wireless sensor networks," Signal Processing Magazine, IEEE, vol. 23, pp. 164-169, 2006.

Song, Yurong, and Guo-Ping Jiang. "Modeling malware propagation in wireless sensor networks using cellular automata." Neural Networks and Signal Processing, 2008 International Conference on. IEEE, 2008.

Stathopoulos, Thanos, John Heidemann, and Deborah Estrin. A remote code update mechanism for wireless sensor networks. No. CENS-TR-30. CALIFORNIA UNIV LOS ANGELES CENTER FOR EMBEDDED NETWORKED SENSING, 2003.

Patel, Neil, David Culler, and Scott Shenker. Trickle: A self-regulating algorithm for code propagation and maintenance in wireless sensor networks. Computer Science Division, University of California, 2003.

Brown, Stephen, and Cormac J. Sreenan. "A new model for updating software in wireless sensor networks." Network, IEEE 20.6 (2006): 42-47.

Wolfram, Stephen. "Universality and complexity in cellular automata." Physica D: Nonlinear Phenomena 10.1 (1984): 1-35.

https://www.cs.purdue.edu/homes/park/interest-ca.html

http://scipy.org/


Refbacks

  • There are currently no refbacks.


Journal of Mobile, Embedded and Distributed Systems (JMEDS) ISSN: 2067 – 4074 (online)