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Detecting Sybil nodes in stationary wireless sensor networks using learning automaton and client puzzles

Detecting Sybil nodes in stationary wireless sensor networks using learning automaton and client puzzles

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A well-known harmful attack against wireless sensor networks (WSNs) is the Sybil attack. In a Sybil attack, WSN is destabilised by a malicious node which forges a large number of fake identities to disrupt network protocols such as routing, data aggregation, and fair resource allocation. In this study, the authors suggest a new algorithm based on a composition of learning automaton (LA) model and client puzzles theory to identify Sybil nodes in stationary WSNs. In the proposed algorithm, each node sends puzzles to its neighbours periodically during the network lifetime and tries to identify Sybil nodes among them, considering their response time (puzzle solving time). In this algorithm, each node equipped with a LA to reduce the communication and computation overhead of sending and solving puzzles. The proposed algorithm has been simulated using J-SIM simulator and simulation results have shown that the proposed algorithm can detect 100% of Sybil nodes and the false detection rate is about 5% on average. Also, the performance of the proposed algorithm has been compared to a wellknown neighbour-based algorithm through experiments and the results have shown that the proposed algorithm is significantly better than this algorithm in terms of detection and false detection rates.

References

    1. 1)
      • 1. Jamshidi, M., Shaltooki, A.A., Dagalzadeh, Z., et al: ‘A dynamic ID assignment mechanism to defend against node replication attack in static wireless sensor networks’, Int. J. Inf. Vis., 2019, 3, (1), pp. 1317.
    2. 2)
      • 2. Douceur, J.R.: ‘The sybil attack, first international workshop on peer-to-peer systems (IPTPS)’ (Springer, Berlin, Heidelberg, 2002), pp. 251260.
    3. 3)
      • 3. Newsome, J., Shi, E., Song, D., et al: ‘The sybil attack in sensor networks: analysis and defenses’. Int. Symp. on Information Processing in Sensor Networks, ACM, Berkeley, CA, USA, April 2004, pp. 259268.
    4. 4)
      • 4. Misra, S., Myneni, S.: ‘On identifying power control performing sybil nodes in wireless sensor networks using RSSI’. Global Telecommunications Conf., Miami, FL, USA, December 2010, pp. 15.
    5. 5)
      • 5. Jamshidi, M., Ranjbari, M., Esnaashari, M., et al: ‘A new algorithm to defend against sybil attack in static wireless sensor networks using mobile observer sensor nodes’, Ad Hoc Sensor Wirel. Netw., 2019, 43, pp. 213238.
    6. 6)
      • 6. Ssu, K.F., Wang, W.T., Chang, W.C.: ‘Detecting sybil attacks in wireless sensor networks using neighboring information’, Comput. Netw., 2009, 53, (18), pp. 30423056.
    7. 7)
      • 7. Rupinder, S., Singh, J., Singh, R.: ‘TBSD: a defend against sybil attack in wireless sensor networks’, Int. J. Comput. Sci. Netw. Secur. (IJCSNS), 2016, 16, (11), pp. 9099.
    8. 8)
      • 8. Dhamodharan, U.S., Vayanaperumal, R.: ‘Detecting and preventing sybil attacks in wireless sensor networks using message authentication and passing method’, Scientific World J., 2015, 1, (1), pp. 1317.
    9. 9)
      • 9. Rafeh, R., Khodadadi, M.: ‘Detecting sybil nodes in wireless sensor networks using two-hop messages’, Indian J. Sci. Technol., 2014, 7, (9), pp. 13591368.
    10. 10)
      • 10. Sarigiannidis, P., Karapistoli, E., Economides, A.: ‘Detecting sybil attacks in wireless sensor networks using UWB ranging-based information’, Expert Syst. Appl., 2015, 42, (21), pp. 75607572.
    11. 11)
      • 11. Tang, Q., Wang, J.: ‘A secure positioning algorithm against sybil attack in wireless sensor networks based on number allocating’. 17th Int. Conf. on Communication Technology (ICCT), Chengdu, China, October 2017, pp. 932936.
    12. 12)
      • 12. Chen, S., Yang, G., Chen, S.: ‘A security routing mechanism against sybil attack for wireless sensor networks’. Int. Conf. on Communications and Mobile Computing, Shenzhen, China, April 2010, pp. 142146.
    13. 13)
      • 13. Jangra, A., Priyanka, S.: ‘Securing LEACH protocol from sybil attack using jakes channel scheme (JCS)’. Int. Conf. on Advances in ICT for Emerging Regions, Sri Lanka Foundation Institute, Colombo, Sri Lanka, 2011, pp. 7987.
    14. 14)
      • 14. Jan, M.A., Nanda, P., He, X., et al: ‘A sybil attack detection scheme for a centralized clustering-based hierarchical network’. Trustcom/BigDataSE/ISPA1, Helsinki, Finland, August 2015, pp. 318325.
    15. 15)
      • 15. 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’, Wirel. Pers. Commun., 2018, 105, (1), pp. 145173.
    16. 16)
      • 16. Piro, C., Shields, C., Levine, B.N.: ‘Detecting the sybil attack in mobile Ad hoc networks’. Securecomm and Workshops, Baltimore, MD, USA, August 2006, pp. 111.
    17. 17)
      • 17. Jamshidi, M., Zangeneh, E., Esnaashari, M., et al: ‘A lightweight algorithm for detecting mobile sybil nodes in mobile wireless sensor networks’, Comput. Electr. Eng., 2017, 64, pp. 220232.
    18. 18)
      • 18. Jamshidi, M., Ranjbari, M., Esnaashari, M., et al: ‘Sybil node detection in mobile wireless sensor networks using observer nodes’, Int. J. Inf. Vis., 2018, 2, (3), pp. 159165.
    19. 19)
      • 19. Jamshidi, M., Darwesh, A.M., Lorenc, A., et al: ‘A precise algorithm for detecting malicious sybil nodes in mobile wireless sensor networks’, IEEE Trans. Smart Process. Comput., 2018, 7, (6), pp. 457466.
    20. 20)
      • 20. Juels, A., Brainard, J.: ‘Client puzzles: a cryptographic countermeasure against connection depletion attacks’. NDSS, 99, 1999, pp. 151165.
    21. 21)
      • 21. Aura, T., Nikander, P., Leiwo, J.: ‘DOS-resistant authentication with client puzzles’. Int. Workshop on Security Protocols, Berlin, Heidelberg, April 2000, pp. 170177.
    22. 22)
      • 22. Dwork, C., Naor, M.: ‘Pricing via processing or combating junk mail’. Annual Int. Cryptology Conf., Berlin, Heidelberg, May 1993, pp. 139147.
    23. 23)
      • 23. Singh, V.P., Jain, S., Singhai, J.: ‘Hello flood attack and its countermeasures in wireless sensor networks’, Int. J. Comput. Sci. Issues, 2010, 7, (11), pp. 2327.
    24. 24)
      • 24. Bocan, V.: ‘Threshold puzzles: the evolution of DOS-resistant authentication’, Periodica Politechnica, Trans. Autom. Control Comput. Sci., 2004, 49, p. 63.
    25. 25)
      • 25. Dong, Q., Gao, L., Li, X.: ‘A new client-puzzle based DoS-resistant scheme of IEEE 802.11i wireless authentication protocol’. 3rd Int. Conf. on Biomedical Engineering and Informatics (BMEI), Yantai, China, October 2010, pp. 27122716.
    26. 26)
      • 26. Ranjbari, M., Torkestani, J.A.: ‘A learning automata-based algorithm for energy and SLA efficient consolidation of virtual machines in cloud data centers’, J. Parallel Distrib. Comput., 2018, 113, pp. 5562.
    27. 27)
      • 27. Esnaashari, M., Meybodi, M.R.: ‘A cellular learning automata-based deployment strategy for mobile wireless sensor networks’, J. Parallel Distrib. Comput., 2011, 71, (7), pp. 9881001.
    28. 28)
      • 28. Jangirala, S., Dheerendra, M., Sourav, M.: ‘Secure lightweight user authentication and key agreement scheme for wireless sensor networks tailored for the internet of things environment’. Int. Conf. on Information Systems Security, Cham, November 2016, pp. 4565.
    29. 29)
      • 29. Dutta, P.K., Hui, J.W., Chu, D.C., et al: ‘Securing the deluge network programming system’. Proc. of the 5th Int. Conf. on Information Processing in Sensor Networks, ACM, Nashville, Tennessee, USA, April 2006, pp. 326333.
    30. 30)
      • 30. Sobeih, A., Hou, J.C., Kung, L.C., et al: ‘J-Sim: a simulation and emulation environment for wireless sensor networks’, IEEE Wirel. Commun., 2006, 13, (4), pp. 104119.
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