A stochastic model of malicious data packet propagation in Mobile Wireless Sensor Networks

被引:0
作者
Wang, Xiao-Ming [1 ]
Li, Cheng-Bo [1 ]
Li, Ying-Shu [2 ,3 ]
机构
[1] School of Computer Science, Shaanxi Normal University
[2] Department of Computer Science, Georgia State University
[3] School of Computer Science and Technology, Harbin Institute of Technology
来源
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2013年 / 35卷 / 06期
关键词
Malicious data packet; Mobile Wireless Sensor Networks (MWSN); Propagation model; Stochastic cellular automaton;
D O I
10.3724/SP.J.1146.2012.00945
中图分类号
学科分类号
摘要
This paper investigates the propagation pattern of malicious data packets in Mobile Wireless Sensor Networks (MWSN) through employing the stochastic cellular automata. The corresponding network model, channel allocation mechanism, signal interference model and malicious packet propagation model are designed based on information communication characteristics of MWSN. The new cellular space, neighborhood and states as well as state transition rules are defined. Then a stochastic cellular automaton model for modeling the propagation process of malicious packets in MWSN is proposed to study the spatial-temporal behavior characteristics of malicious data packet propagation in the uncertain environment. The results show that the proposed model can efficiently predict the propagation trend and the spatial distribution state of malicious data packets in MWSN, and overcome the disadvantages that no one of the Markov Random Field, the differential equation and the difference equation can describe the spatial distribution state of malicious data packet propagation in MWSN.
引用
收藏
页码:1290 / 1297
页数:7
相关论文
共 16 条
  • [1] Yick J., Mukherjee B., Ghosal D., Wireless sensor network survey, Computer Networks, 52, 12, pp. 2292-2330, (2008)
  • [2] Kevin L.M., A brief survey of self-organization in wireless sensor networks, Wireless Communications and Mobile Computing, 7, 7, pp. 823-834, (2007)
  • [3] Martin J.C., Burge III L.L., Gill J.I., Et al., Modeling the spread of mobile malware, International Journal of Computer Aided Engineering and Technology, 1, 2, pp. 3-14, (2010)
  • [4] Yang G., Wang A.-Q., Cheng Z.-Y., Et al., An energysaving privacy-preserving data aggregation algorithm, Chinese Journal of Computers, 34, 5, pp. 792-800, (2011)
  • [5] Khouzani M.H.R., Sarkar S., Altman E., Saddle-point strategies in malware attack, IEEE Journal of Selected Areas in Communications, 30, 1, pp. 1-13, (2012)
  • [6] Tripathy S., Nandi S., Defense against outside attacks in wireless sensor networks, Computer Communications, 31, 4, pp. 818-826, (2008)
  • [7] Song Y.-R., Jiang G.-P., Xu J.-G., An epidemic spreading model in adaptive networks based on cellular automata, Acta Physica Sinica, 60, 12, (2011)
  • [8] Cheng S.M., Ao W.C., Chen P.Y., Et al., On modeling malware propagation in generalized social networks, IEEE Communications Letters, 15, 1, pp. 25-27, (2011)
  • [9] Bettstetter C., Mobility modeling in wireless networks: categorization, smooth movement, border effects, Mobile Computing and Communications Review, 5, 3, pp. 55-66, (2001)
  • [10] Wang X.-M., Li Q.-L., Li Y.-S., EiSIRS: a formal model to analyze the dynamics of worm propagation in wireless sensor networks, Journal of Combinatorial Optimization, 20, 1, pp. 47-62, (2010)