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Detection of Attacks on Wireless Sensor Network Using Genetic Algorithms Based on Fuzzy

1Altinbas University, Istanbul, Turkey

2Turkish Aeronautical Association University, Ankara, Turkey

Published: 2 Feb 2019.
Editor(s): H Hadiyanto

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Abstract

In this paper an individual - suitable function calculating design for WSNs is conferred. A multi-agent- located construction for WSNs is planned and an analytical type of the active combination is built for the function appropriation difficulty. The purpose of this study is to identify the threats identified by clustering genetic algorithms in clustering networks, which will prolong network lifetime. In addition, optimal routing is done using the fuzzy function. Simulation results show that the simulated genetic algorithm improves diagnostic speed and improves energy consumption.

©2019. CBIORE-IJRED. All rights reserved

Article History: Received May 16th 2018; Received in revised form October 6th 2018; Accepted January 6th 2019; Available online

How to Cite This Article: Al-Hayali, S., Ucan, O., Rahebi, J. and Bayat, O. (2019) Detection of Attacks on Wireless Sensor Network Using Genetic Algorithms Based on Fuzzy. International Journal of Renewable Energy Development, 8(1), 57-64.

https://doi.org/10.14710/ijred.8.1.57-64

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Keywords: wireless sensor network; fuzzy, genetic algorithm; detection of attack; malicious node

Article Metrics:

  1. Abbasi, A. A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer communications, 30(14-15), 2826-2841
  2. Abdelhak, S., Gurram, C. S., Ghosh, S., & Bayoumi, M. (2010). Energy-balancing task allocation on wireless sensor networks for extending the lifetime. Paper presented at the Circuits and Systems (MWSCAS), 2010 53rd IEEE International Midwest Symposium on
  3. Agah, A., Das, S. K., Basu, K., & Asadi, M. (2004). Intrusion detection in sensor networks: A non-cooperative game approach. Paper presented at the Network Computing and Applications, 2004.(NCA 2004). Proceedings. Third IEEE International Symposium on
  4. Al-Karaki, J. N., & Kamal, A. E. (2004). Routing techniques in wireless sensor networks: a survey. IEEE wireless communications, 11(6), 6-28
  5. Anandh, S. J., & Baburaj, E. (2016). An Improved Energy Balanced Dissimilar Clustered Routing Architecture for Wireless Sensor Networks. Circuit and Systems, Scientific Research
  6. Arboleda, L. M., & Nasser, N. (2006). Comparison of clustering algorithms and protocols for wireless sensor networks. Paper presented at the Electrical and Computer Engineering, 2006. CCECE'06. Canadian Conference on
  7. Banerjee, S., Mukhopadhyay, D., & Roy, S. (2007). Defending against sybil attacks in sensor networks: Google Patents
  8. Boyinbode, O., Le, H., & Takizawa, M. (2011). A survey on clustering algorithms for wireless sensor networks. International Journal of Space-Based and Situated Computing, 1(2-3), 130-136
  9. Choi, K., Yun, M., Chae, K., & Kim, M. (2012). An enhanced key management using ZigBee Pro for wireless sensor networks. Paper presented at the Information Networking (ICOIN), 2012 International Conference on
  10. da Silva, A. P. R., Martins, M. H., Rocha, B. P., Loureiro, A. A., Ruiz, L. B., & Wong, H. C. (2005). Decentralized intrusion detection in wireless sensor networks. Paper presented at the Proceedings of the 1st ACM international workshop on Quality of service & security in wireless and mobile networks
  11. Demirbas, M., & Song, Y. (2006). An RSSI-based scheme for sybil attack detection in wireless sensor networks. Paper presented at the Proceedings of the 2006 International Symposium on on World of Wireless, Mobile and Multimedia Networks
  12. Doumit, S. S., & Agrawal, D. P. (2003). Self-organized criticality and stochastic learning based intrusion detection system for wireless sensor networks. Paper presented at the Military Communications Conference, 2003. MILCOM'03. 2003 IEEE
  13. Duarte-Melo, E. J., & Liu, M. (2002). Analysis of energy consumption and lifetime of heterogeneous wireless sensor networks. Paper presented at the Global Telecommunications Conference, 2002. GLOBECOM'02. IEEE
  14. Elbaşı, E., & Suat, Ö. (2012). Secure data aggregation in wireless multimedia sensor networks via watermarking. Paper presented at the Application of Information and Communication Technologies (AICT), 2012 6th International Conference on
  15. Gerla, M., & Tsai, J. T.-C. (1995). Multicluster, mobile, multimedia radio network. Wireless networks, 1(3), 255-265
  16. Giannecchini, S., Caccamo, M., & Shih, C.-S. (2004). Collaborative resource allocation in wireless sensor networks. Paper presented at the Real-Time Systems, 2004. ECRTS 2004. Proceedings. 16th Euromicro Conference on
  17. Goudar, C. P., & Kulkarni, S. S. (2015). Mechanisms for detecting and preventing denial of sleep attacks and strengthening signals in wireless sensor networks. Int. J. Emerg. Res. Manag. Technol, 4(6)
  18. Guo, L., & Tang, Q. (2010). An improved routing protocol in WSN with hybrid genetic algorithm. Paper presented at the Networks Security Wireless Communications and Trusted Computing (NSWCTC), 2010 Second International Conference on
  19. Gupta, S., Kar, S., & Dharmaraja, S. (2011). WHOP: Wormhole attack detection protocol using hound packet. Paper presented at the Innovations in information technology (IIT), 2011 international conference on
  20. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on wireless communications, 1(4), 660-670
  21. Khanna, R., Liu, H., & Chen, H.-H. (2009). Reduced complexity intrusion detection in sensor networks using genetic algorithm. Paper presented at the Communications, 2009. ICC'09. IEEE International Conference on
  22. Lin, J., Xiao, W., Lewis, F. L., & Xie, L. (2009). Energy-efficient distributed adaptive multisensor scheduling for target tracking in wireless sensor networks. IEEE Transactions on Instrumentation and Measurement, 58(6), 1886-1896
  23. Liu, F., Cheng, X., & Chen, D. (2007). Insider attacker detection in wireless sensor networks. Paper presented at the INFOCOM 2007. 26th IEEE International Conference on Computer Communications. IEEE
  24. Maimour, M., Zeghilet, H., & Lepage, F. (2010). Cluster-based Routing Protocols for Energy-Efficiency in Wireless Sensor Networks Sustainable Wireless Sensor Networks: Intech
  25. Mhatre, V., & Rosenberg, C. (2004). Homogeneous vs heterogeneous clustered sensor networks: a comparative study. Paper presented at the Communications, 2004 IEEE International Conference on
  26. Newsome, J., Shi, E., Song, D., & Perrig, A. (2004). The sybil attack in sensor networks: analysis & defenses. Paper presented at the Proceedings of the 3rd international symposium on Information processing in sensor networks
  27. Parno, B., Perrig, A., & Gligor, V. (2005). Distributed detection of node replication attacks in sensor networks. Paper presented at the Security and Privacy, 2005 IEEE Symposium on
  28. Peres, M., Chalouf, M. A., & Krief, F. (2011). On optimizing energy consumption: An adaptative authentication level in wireless sensor networks. Paper presented at the Global Information Infrastructure Symposium (GIIS), 2011
  29. Pires, W., de Paula Figueiredo, T. H., Wong, H. C., & Loureiro, A. A. F. (2004). Malicious node detection in wireless sensor networks. Paper presented at the Parallel and distributed processing symposium, 2004. Proceedings. 18th international
  30. Piro, C., Shields, C., & Levine, B. N. (2006). Detecting the sybil attack in mobile ad hoc networks. Paper presented at the Securecomm and Workshops, 2006
  31. Rafik, M. B. O., & Mohammed, F. (2013). The impact of ECC's scalar multiplication on wireless sensor networks. Paper presented at the Programming and Systems (ISPS), 2013 11th International Symposium on
  32. Raymond, D. R., Marchany, R. C., Brownfield, M. I., & Midkiff, S. F. (2009). Effects of denial-of-sleep attacks on wireless sensor network MAC protocols. IEEE transactions on vehicular technology, 58(1), 367-380
  33. Sarkar, A., & Murugan, T. S. (2016). Routing protocols for wireless sensor networks: What the literature says? Alexandria Engineering Journal, 55(4), 3173-3183
  34. Younis, O., Krunz, M., & Ramasubramanian, S. (2006). Node clustering in wireless sensor networks: Recent developments and deployment challenges. IEEE network, 20(3), 20-25
  35. Zhu, S., Setia, S., & Jajodia, S. (2006). LEAP+: Efficient security mechanisms for large-scale distributed sensor networks. ACM Transactions on Sensor Networks (TOSN), 2(4), 500-528

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Last update: 2024-10-11 16:24:41

  1. Improved Performance Using Fuzzy Possibilistic C-Means Clustering Algorithm in Wireless Sensor Network

    Shweta Kushwaha, Kuldeep Singh Jadon. 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT), 2020. doi: 10.1109/CSNT48778.2020.9115740