A New Efficient Defense Strategy against Byzantine Attack in Wireless Sensor Networks

Document Type : Original Article

Authors

Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

Abstract

Wireless sensor networks’ characteristics and limitations have led to severe challenges in their security. In this paper, one of the most common attacks, named as the Byzantine attack, has been studied. A sensor network with a mobile data fusion center is considered, and an optimized data collecting method is proposed which can apply the Neyman-Pearson criterion in a particular case of hard decision making. It means that keeping the false alarm rate constant, the detection probability is maximized. Using statistical data analysis, a closed model is obtained with an acceptable speed of convergence in a various number of sensors and attackers in the network. Then a novel malicious sensor detection scheme is exploited to identify the attack strength using the Hamming distance between every sensor report and the final decision. The simulation results show the superior performance of applying the proposed method in the decision process and also the proposed attack detection method in comparison with the conventional methods.

Keywords

Main Subjects


[1] C. Chong and S. P. Kumar, “Sensor networks: evolution, opportunities, and challenges,” in Proceedings of the IEEE, vol. 91, no. 8, pp. 1247-1256, Aug. 2003.
[2] C. Karlof and D. Wagner, “Secure Routing in Sensor Networks: Attacks and countermeasures” In Proc. Of First IEEE International Workshop on Sensor Network Protocols and Applications, May 2003.
[3] L. Lamport, R. Shostak, M. Pease, “The Byzantine Generals Problem”, ACM Trans. Programming Languages and Systems, vol. 4, no. 3, pp. 382-401, Jul. 1982.
[4] Y. Wang, G. Attebury and B. Ramamurthy, “A survey of security issues in wireless sensor networks,” in IEEE Communications Surveys & Tutorials, vol. 8, no. 2, pp. 2-23, Second Quarter 2006.
[5] Y. Brun, G. Edwards, J. Y. Bang, and N. Medvidovic, “Smart redundancy for distributed computation,” in 31st International Conference on Distributed Computing Systems (ICDCS), pp. 665 -676, Jun. 2011.
[6] P. Sridhar, A. Madni, and M. Jamshidi, “Hierarchical aggregation and intelligent monitoring and control in fault-tolerant wireless sensor net-works,” IEEE Systems Journal, vol. 1, no. 1, pp. 38-54, Sep. 2007.
[7] F. Liu, X. Cheng and D. Chen, “Insider attacker detection in wireless sensor networks,” in Proc. of IEEE Conference on Computer Commu-nications (Infocom), 2007.
[8] W. Zhang, S. K. Das and Y. Liu, “A Trust Based Framework for Secure Data Aggregation in Wireless Sensor Networks,” 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks, Reston, VA, pp. 60-69, 2006.
[9] S. Marano, V. Matta, “Distributed detection in the presence of byzantine attacks,” IEEE Transactions on Signal Processing, vol. 57, no. 1, pp. 16-29, Jan. 2009.
[10] E. Soltanmohammadi, M. Orooji, and M. N. Pour, “Decentralized Hypothesis Testing in Wireless Sensor Networks in the Presence of Misbehaving Nodes”, IEEE Transaction on Information Forensics and Security, vol. 8, no. 1, Jan. 2013.
[11] A. Vempaty, L. Tong, and P. K. Varshney, “Distributed inference with Byzantine data,” IEEE Signal Process. Mag., vol. 30, no. 5, pp. 65-75, Sep. 2013.
[12] R. Curtmola and C. Nita-Rotaru, “BSMR: Byzantine-resilient secure multicast routing in multihop wireless networks,” IEEE Transactions onMobile Computing, vol. 8, no. 4, pp. 445-459, Apr. 2009.
[13] Q. Yang, J. Yang, W. Yu, D. An, N. Zhang, and W. Zhao, ”On false data injection attacks against power system state estimation: Modeling and countermeasures,” IEEE Transactions on Parallel and DistributedSystems, vol. 25, no. 3, pp. 717-729, Mar. 2014.
[14] L. Zhang, G. Ding, Q. Wu, Y. Zou, Z. Han, J. Wang, "Byzantine attack and defense in cognitive radio networks: A survey", IEEE Commun. Surveys Tuts., vol. 17, no. 3, pp. 1342-1363, Apr. 2015.
[15] A. A. Sharifi and M. J. Musevi Niya, “Defense Against SSDF Attack in Cognitive Radio Networks: Attack-Aware Collaborative Spectrum Sensing Approach,” in IEEE Communications Letters, vol. 20, no. 1, pp. 93-96, Jan. 2016.
[16] N. Choudhary, P. Dabas, “An Enhanced Mechanism to Detect and Prevent Byzantine Attack in Wireless Network based on CBDS,” in Journal of Network Communications and Emerging Technologies, vol. 7, no. 7, pp. 25-28, Jul. 2017.
[17] X. Ren, J. Yan and Y. Mo, “Binary Hypothesis Testing With Byzantine Sensors: Fundamental Tradeoff Between Security and Efficiency,” in IEEE Transactions on Signal Processing, vol. 66, no. 6, pp. 1454-1468, Mar. 2018.
[18] G. Mergen, Z. Qing, and L. Tong, “Sensor networks with mobile access: Energy and capacity considerations,” IEEE Transactions on Communications, vol. 54, no. 11, pp. 2033-2044, Nov. 2006.
[19] H.  Wang,  L.  Lightfoot,  and  T.  Li,  “On  PHY-layer  security  of cognitive radio: Collaborative sensing under malicious attacks,” 44th Annual Conference on Information Sciences and Systems (CISS), pp. 1-6, Mar. 2010.
[20] M. Abdelhakim, L. E. Lightfoot, J. Ren and T. Li, “Distributed Detection in Mobile Access Wireless Sensor Network Under Byzantine Attacks,” IEEE Transactions on parallel and Distributed system, vol. 13, pp. 1045-9219. Apr. 2013.
[21] P. K. Varshney, Distributed detection and data fusion, Springer-Verlag, 1997.
[22] P.  Tan,  M.  Steinbach,  and  V.  Kumar,  Introduction  to  Data  Mining, Addison Wesley, 2006.