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access icon free Lightweight cloned-node detection algorithm for efficiently handling SSDF attacks and facilitating secure spectrum allocation in CWSNs

© The Institution of Engineering and Technology
Received 22/07/2016, Accepted 04/01/2018, Revised 23/08/2017, Published 08/01/2018

Cognitive Wireless Sensor Networks (CWSNs) provide better bandwidth utilization when compared with normal wireless sensor networks. CWSNs use a technique called opportunistic spectrum access for data transfer. While doing so, however, CWSNs are subject to several security threats. The spectrum sensing data falsification attack comes under the DoS attack. In this attack, a malicious node sends a modified spectrum sensing report so that the resulting collaborative spectrum sensing decision becomes wrong and a good cognitive sensor node receives a wrong decision regarding the vacant spectrum band of other's network. In the presence of the node cloning attack, the solution of the SSDF attack becomes even more difficult. In the node cloning attack, the malicious node creates many clones of the compromised node in the network. In order to confuse the collaborative spectrum sensing system, the clone nodes can send false spectrum sensing reports in a large number. The maximum-match filtering (MMF) algorithm is used for making a secure spectrum sensing decision in CWSNs. The Cloned-Node Detection (CND) algorithm is proposed here to detect cloned nodes. This study also explains how the CND algorithm assists the MMF algorithm to make better spectrum sensing decisions by avoiding the node cloning attack.

Inspec keywords: filtering theory; wireless sensor networks; radio spectrum management

Other keywords: SSDF attacks; DoS attack; denial of service attack; maximum-match filtering algorithm; CWSN; node cloning attack; MMF algorithm; collaborative spectrum sensing decision; CND algorithm; malicious node; cloned-node detection algorithm; lightweight cloned-node detection algorithm; secure spectrum allocation; cognitive wireless sensor networks

Subjects: Filtering methods in signal processing; Wireless sensor networks

References

    1. 1)
      • 20. Zhang, M., Vishal, K., Shigang, C., et al: ‘Memory efficient protocols for detecting node replication attacks in wireless sensor networks’. 16th IEEE Int. Conf. Network Protocols (ICNP'09), 2009, pp. 284–293.
    2. 2)
      • 31. Rastegarnia, A., Solouk, V.: ‘Performance evaluation of castalia wireless sensor network simulator’. 34th Int. Conf. Telecommunications and Signal Processing (TSP), 2011.
    3. 3)
      • 10. Brown, T.X., Sethi, A.: ‘Potential cognitive radio denial-of-service vulnerabilities and protection countermeasures: a multi-dimensional analysis and assessment’. 2nd Int. Conf. Cognitive Radio Oriented Wireless Networks and Communications (CrownCom), 2007.
    4. 4)
      • 29. Araujo, A., Romero, E., Blesa, J., et al: ‘Cognitive wireless sensor networks framework for green communications design’. Second Int. Conf. Advances in Cognitive Radio, 2012, pp. 34–40.
    5. 5)
      • 21. Chan, H., Perrig, A., Song, D.X.: ‘Random key predistribution schemes for sensor networks’. IEEE Symp. Security and Privacy, IEEE Computer Society, 2003, pp. 197–213.
    6. 6)
      • 8. Arkoulis, S., Kazatzopoulos, L., Delakouridis, C., et al: ‘Cognitive spectrum and its security issues’. 2nd Int. Conf. Next Generation Mobile Applications, Services and Technologies (NGMAST), 2008, pp. 565–570.
    7. 7)
      • 7. Federal Communication Commission: ‘Notice for proposed rule making (NPRM 03-22), facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies’, 2003.
    8. 8)
      • 30. Ray, A., De, D.: ‘Energy efficient clustering protocol based on K-means (EECPK-means)-midpoint algorithm for enhanced network lifetime in wireless sensor network’, IET Wirel. Sens. Syst., 2016, 6, pp. 181–191.
    9. 9)
      • 14. Khan, W.Z., Mohammed, Y.A., Xiang, Y.: ‘Detection and mitigation of node replication attacks in wireless sensor networks: a survey’, Int. J. Distrib. Sensor Netw., 2013, pp. 1–22.
    10. 10)
      • 13. Newsome, J., Shi, E., Song, D., et al: ‘The sybil attack in sensor networks: analysis and defenses’, Research Showcase at CMU, the Carnegie Institute of Technology, IPSN'04, ACM, 2004, 4, pp. 259–268.
    11. 11)
      • 22. Yu, C.M., Lu, C.S., Kuo, S.Y.: ‘CSI: compressed sensing based clone identification in sensor networks’. IEEE Int. Conf. Pervasive Computing and Communications Workshops (PERCOM Workshops), 2012.
    12. 12)
      • 15. Lou, Y., Zhang, Y., Liu, S.: ‘Single Hop detection of node clone attacks in mobile wireless sensor networks’. Int. Workshop on Information and Electronics Engineering (IWIEE), 2012, pp. 2798–2803.
    13. 13)
      • 6. Chen, R., Park, J.M.: ‘Ensuring trustworthy spectrum sensing in cognitive radio networks’. 1st IEEE Workshop on Networking Technologies for Software Defined Radio Networks (SDR'06), 2006, pp. 110–119.
    14. 14)
      • 2. Sen, J.: ‘Security and privacy challenges in cognitive wireless sensor Networks’ (IGI Global, Hershey, Pennsylvania, USA, 2013), pp. 1–36.
    15. 15)
      • 11. Araujo, A., Blesa, J., Romero, E., et al: ‘Security in cognitive wireless sensor networks. Challenges and open problems’, EURASIP J. Wirel. Commun. Netw., 2012, 48, pp. 1–8.
    16. 16)
      • 5. Chatterjee, P.S., Roy, M.: ‘Base station controlled spectrum allocation technique to detect the PUE attack in CWSN’, Comput. Intell. Data Min., 2015, 1, pp. 689–698.
    17. 17)
      • 28. Mitzenmacher, M.: ‘Compressed bloom filters’. 20th Annual ACM Symp. Principles of Distributed Computing, 2001, pp. 144–150.
    18. 18)
      • 16. Fan, B., Andersen, D.G., Kaminsky, M, et al: ‘Cuckoo filter: practically better than bloom’. 10th ACM Int. Conf.n Emerging Networking Experiments and Technologies, 2014, pp. 75–88.
    19. 19)
      • 1. Ozgur, B.A., Karli, O.B., Ergul, O.: ‘Cognitive radio sensor networks’, IEEE Netw., 2009, 23, pp. 34–40.
    20. 20)
      • 27. Neter, J., Kutner, M.H., Nachtsheim, C.J., et al: ‘Applied linear statistical models’ (McGraw-Hill, Irwin, 5th edn.).
    21. 21)
      • 23. Chowdhury, S.H., Grebur, B., Xiao, L.: ‘Defence against spectrum sensing data falsification attacks in cognitive radio networks’. Security and Privacy in Communication Networks, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunication Engineering, 2012, pp. 154–171.
    22. 22)
      • 18. Parno, B., Perrig, A., Gligor, V.: ‘Distributed detection of node replication attacks in sensor networks’. IEEE Symp. Security and Privacy, 2005, pp. 49–63.
    23. 23)
      • 4. Haykin, S.: ‘Cognitive radio: brain-empowered wireless communications’, IEEE J. Sel. Areas Commun., 2005, 23, pp. 201–220.
    24. 24)
      • 3. Chen, W., Niu, Y., Zou, Y.: ‘Congestion control and energy-balanced scheme based on the hierarchy for WSNs’, IET Wirel. Sens. Syst., 2017, 7, (1), pp. 1–8.
    25. 25)
      • 12. Chatterjee, P.S., Roy, M.: ‘A regression based spectrum-sensing data-falsification attack detection technique in CWSN’. 14th Int. Conf. Information Technology, 2015, pp. 48–53.
    26. 26)
      • 26. Chen, C., Song, M., Xin, C., et al: ‘A robust malicious user detection scheme in cooperative spectrum sensing’. IEEE Global Telecommun. Conf., 2012.
    27. 27)
      • 9. Chen, R., Park, J.M., Bian, K.: ‘Robust distributed spectrum sensing in cognitive radio networks’. IEEE Conf. Computer Communications (Infocom), 2008, pp. 31–35.
    28. 28)
      • 24. Kaligineedi, P., Khabbazian, M., Bhargava, V.K.: ‘Malicious user detection in a cognitive radio cooperative sensing system’, IEEE Trans. Wirel. Commun., 2010, 9, pp. 2488–2497.
    29. 29)
      • 25. Li, H., Han, Z.: ‘Catch me if you can: an abnormality detection approach for collaborative spectrum sensing in cognitive radio networks’, IEEE Trans. Wirel. Commun., 2010, pp. 3554–3565.
    30. 30)
      • 19. Conti, M., Pietro, D.R., Mancini, L.V., et al: ‘A randomized, efficient, and distributed protocol for the detection of node replication attacks in wireless sensor networks’. 8th ACM Int. Symp. Mobile Ad hoc Networking and Computing (MobiHoc ‘07), 2007, pp. 80–89.
    31. 31)
      • 17. Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: ‘Energy-efficient communication protocol for wireless microsensor networks’. Hawaii Int. Conf. System Sciences, 2000, pp. 1–10.
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