Abstract
Today, Wireless Sensor Networks are widely employed in various applications including military, environment, medical and urban applications. Thus, security establishment in such networks is of great importance. One of the dangerous attacks against these networks is Sybil attack. In this attack, malicious node propagates multiple fake identities simultaneously which affects routing protocols and many other operations like voting, reputation evaluation, and data aggregation. In this paper, first, a novel model of Sybil attack in cluster-based sensor networks is proposed. In the proposed attack model, a malicious node uses each of its Sybil identity to join each cluster in the network. Thus, the malicious node joins many clusters of the network simultaneously. In this paper, also a distributed algorithm based on Received Signal Strength Indicator and positioning using three points to defend against the novel attack model is proposed. The proposed algorithm is implemented and its efficiency in terms of true detection rate, false detection rate, and communication overhead is evaluated through a series of experiments. Experiment results show that the proposed algorithm is able to detect 99.8% of Sybil nodes with 0.008% false detection rate (in average). Additionally, the proposed algorithm is compared with other algorithms in terms of true detection rate and false detection rate which shows that the proposed algorithm performs desirably.
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Muthukumaran, K., Chitra, K., & Selvakumar, C. (2017). An energy efficient clustering scheme using multilevel routing for wireless sensor network. Computers & Electrical Engineering (in press).
Douceur, J. R. (2002). The Sybil attack. First international workshop on peer-to-peer systems (IPTPS ‘02).
Conti, M., Pietro, D. R., & Spognardi, A. (2014). Clone wars: Distributed detection of clone attacks in mobile WSNs. Journal of Computer and System Sciences, 80(3), 654–669.
Shafiei, H., Khonsari, A., Derakhshi, H., & Mousavi, P. (2014). Detection and mitigation of sinkhole attacks in wireless sensor networks. Journal of Computer and System Sciences, 80(3), 644–653.
And, K. C., & Wagner, D. (2003). Secure routing in wireless sensor networks: Attacks and countermeasures. AdHoc Networks, 1(2), 299–302.
Liu, G., Yan, Z., & Pedrycz, W. (2018). Data collection for attack detection and security measurement in mobile ad hoc networks: A survey. Journal of Network and Computer Applications, 105, 105–122.
Newsome, J., Shi, E., Song, D., & Perrig, A. (2004). The Sybil attack in sensor networks: Analysis and defenses. In International symposium on information processing in sensor networks (pp. 259–268).
Chen, S., Yang, G., & Chen, S. (2010). A security routing mechanism against Sybil attack for wireless sensor networks. In International conference on communications and mobile computing (pp. 142–146).
Jangra, A., & Priyanka, S. (2011). Securing LEACH protocol from Sybil attack using jakes channel scheme (JCS). In International conferences on advances in ICT for emerging regions (ICTer).
Vasudeva, A., & Sood, M. (2012). Sybil attack on lowest id clustering algorithm in the mobile ad hoc network. International Journal of Network Security & Its Applications, 4(5), 135–147.
Zhong, S., Li, L., Liu, Y. G., & Yang, Y. R. (2004). Privacy-preserving location based services for mobile users in wireless networks. Technical report YALEU/DCS/TR-1297, Yale Computer Science.
Demirbas, M., & Song, Y. (2006). An RSSI-based scheme for Sybil attack detection in wireless sensor networks. In International symposium on world of wireless, mobile and multimedia networks (pp. 564–570).
Ssu, K. F., Wang, W. T., & Chang, W. C. (2009). Detecting Sybil attacks in wireless sensor networks using neighboring information. Computer Networks, 53(18), 3042–3056.
Ramachandran, S., & Shanmugan, V. (2011). Impact of Sybil and wormhole attacks in location based geographic multicast routing protocol for wireless sensor networks. Journal of Computer Science, 7(7), 973–979.
Misra, S., & Myneni, S. (2010). On identifying power control performing Sybil nodes in wireless sensor networks using RSSI. In InGlobal telecommunications conference (pp. 1–5).
Muraleedharan, R., Ye, X., & Osadciw, L. A. (2008). Prediction of Sybil attack on WSN using bayesian network and swarm intelligence. In Wireless sensing and processing, Orlando, FL, USA.
Wen, M., Li, H., & Zheng, Y.-F. (2008). TDOA-based Sybil attack detection scheme for wireless sensor. Journal of Shanghai University, 12(1), 66–70.
Zhang, Y., Fan, K. F., Zhang, S. B., & Mo, W. (2010). AOA based trust evaluation scheme for Sybil attack detection in WSN. Journal on Application Research of Computers, 27(5), 1847–1849.
Butler, K. R., Ryu, S., Traynor, P., & McDaniel, P. D. (2007). Leveraging identity-based cryptography for node ID assignment in structured P2P systems. Advanced Information Networking and Application Workshops, 20(12), 1803–1815.
Li, F., Mittal, P., Caesar, M., & Borisov N. (2012). Sybil control: Practical Sybil defense with computational puzzles. In Seventh ACM workshop on Scalable trusted computing (pp. 67–68).
Taol, F., & Ma, J.-F. (2008). New approach against Sybil attack in wireless sensor networks. Tongxin Xuebao/Journal on Communications, 29, 13–19.
Zhang, Q., Wang, P., Reeves, D., & Ning, P. (2005). Defending against Sybil attacks in sensor networks. In Second international workshop on security in distributed computing systems (pp. 85–191).
Wang, J., Yang, G., Sun, Y., & Chen, S. (2007). Sybil attack detection based on RSSI for wireless sensor network. In International conference on wireless communications, networking and mobile computing (pp. 2684–2687).
Yang, J., Chen, Y., & Trappe, W. (2008). Detecting Sybil attack in wireless and sensor networks using cluster analysis. In 5th IEEE international conference on mobile ad hoc and sensor systems. Atlanta, GA (pp. 834–839).
Wang, X.-D., Sun, Y.-Q., & Meng, X.-X. (2009). Cluster-based defending mechanism for Sybil attacks in wireless sensor network. Computer Engineering, 15, 47.
Ahmad, J. M., Nanda, P., He, X., & Liu, R. P. (2015). A Sybil attack detection scheme for a centralized clustering-based hierarchical network. Trustcom/BigDataSE/ISPA, 1, 318–325.
Sweety, S., & Sejwar, V. (2014). Sybil attack detection and analysis of energy consumption in cluster based sensor networks. International Journal of Grid and Distributed Computing, 7(5), 15–30.
Almas, S. R., Faez, K., Eshghi, F., & Kelarestaghi, M. (2017). A new lightweight watchdog-based algorithm for detecting Sybil nodes in mobile WSNs. Future Internet, 10(1), 1–17.
Rupinder, S., Singh, J., & Singh, R. (2016). TBSD: A defend against Sybil attack in wireless sensor networks. International Journal of Computer Science and Network Security, 16(11), 90.
Dhamodharan, U. S., & Vayanaperumal, R. (2015). Detecting and preventing Sybil attacks in wireless sensor networks using message authentication and passing method. The Scientific World Journal, 1(1), 13–17.
Amuthavalli, R., & Bhuvaneswaran, R. S. (2014). Detection and prevention of Sybil attack in wireless sensor network employing random password comparison method. Journal of Theoretical & Applied Information Technology, 67(1), 236–246.
Shi, W., Liu, S., & Zhang, Z. (2015). A lightweight detection mechanism against Sybil attack in wireless sensor network. KSII Transactions on Internet & Information Systems, 9(9), 3738–3749.
Sinha, S., Paul, A., & Pal, S. (2014). Use of spline curve in Sybil attack detection based on received signal power-new approach. International Journal on Recent Trends in Engineering & Technology, 11(1), 602–611.
Rafeh, R., & Khodadadi, M. (2014). Detecting Sybil nodes in wireless sensor networks using two-hop messages. Indian Journal of Science and Technology, 7(9), 1359–1368.
Panagiotis, S., Karapistoli, E., & Economides, A. (2015). Detecting Sybil attacks in wireless sensor networks using UWB ranging-based information. Expert Systems with Applications, 42(21), 7560–7572.
Hu, R.-H., Dong, X.-M., & Wang, D.-L. (2015). Defense mechanism against node replication attacks and sybil attacks in wireless sensor networks. Acta Electronica Sinica, 43(4), 744–752.
Jamshidi, M., Zangeneh, E., Esnaashari, M., & Meybodi, M. R. (2017). A lightweight algorithm for detecting mobile Sybil nodes in mobile wireless sensor networks. Computers & Electrical Engineering, 64, 220–232.
Jamshidi, M., Ranjbari, M., Esnaashari, M., Qader, N. N., & Meybodi, M. R. (2018). Sybil node detection in mobile wireless sensor networks using observer nodes. International Journal on Informatics Visualization, 2(3), 159–165.
Yale, P. B. (1968). Geometry and symmetry. Holden-Day.
JSIM Simulator. https://sites.google.com/site/jsimofficial/. Accessed March 21, 2017.
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Jamshidi, M., Zangeneh, E., Esnaashari, M. et al. A Novel Model of Sybil Attack in Cluster-Based Wireless Sensor Networks and Propose a Distributed Algorithm to Defend It. Wireless Pers Commun 105, 145–173 (2019). https://doi.org/10.1007/s11277-018-6107-5
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DOI: https://doi.org/10.1007/s11277-018-6107-5