A Novel Hybrid and Secure Clustering Black hole Attacks Mitigation Technique in Wireless LAN

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Wireless LAN is a dynamic network with large number of mobile nodes. As the traffic increases over the wireless, it will lead to number of problems like congestion and packet loss. This congestion and packet loss problems occurs due to the attacks in wireless LAN.Out of the various attacks black hole attack is most dangerous attack which drops all of the packets received from the source node and which act as a black hole in the universe. In this paper we are providing solution against this attack. We propose a Novel Hybrid and Secure Clustering Black hole Attack Mitigation Technique in Wireless LAN. This technique firstly detects the black hole attack by using threshold values against different parameters, after this clustering approach is used for secure path from source to destination by reducing overhead in the network. Most of existing mechanisms are not as efficient because by isolating black hole attack overhead is increased. A HSBM approach has remarkable advantage over these existing techniques. We simulate the proposed technique by using NS2 simulator and proved that our technique effectively detects the black hole attack in terms of throughput, packet loss, end to end packet delivery ratio, delay.

     

     


  • Keywords


    Black hole attack; wireless LAN.

  • References


      [1] Haitao Wu, Yong Peng, Keping Long, Shiduan Cheng, Jian Ma (2002), “Performance of Reliable Transport Protocol over IEEE 802.11 Wireless LAN:Analysis and Enhancement” IEEE INFOCOM 2002.

      [2] Bellaaj H., Ketata R. and Hsini A (2007), "Fuzzy approach for 802.11wireless intrusion detection", 4th International Conference: Sciences of Electronic, Technologies of Information and Telecommunications, March 25-29, Tunisia.

      [3] Waliullah, M. D. G. (2014). “Wireless LAN Security Threats & Vulnerabilities” (IJACSA) International Journal of Advanced Computer Science and Applications, 5(1), 176–183. https://doi.org/10.1017/CBO9781107415324.004

      [4] Banerjee, S.; Majumder, K. A Survey of Blackhole Attacks and Countermeasures in Wireless Mobile Ad-hoc Networks. Springer.Volume 335, of the series Communications in Computer and Information Science.pp 396-407.

      [5] Rajesh, M.; Usha, G. (2016) “A Novel Honeypot Based Detection and Isolation Approach (NHBADI) to Detect and Isolate Black Hole Attacks in MANET”, Wireless PersonalCommunication. Springer, New York.

      [6] Monika (2016) “Black Hole Attack Detection and Prevention in Wireless Networks”, International Research Journal of Engineering and Technology (IRJET), Volume: 03 Issue: 06 page 1376-1380.

      [7] Anand, A.; Bhandari, A. (2014) “Prevention of Black Hole Attack on AODV in MANET using hash function”, Proceeding of 3rd international conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions).

      [8] Jaisankar, N.; Saravanan, R.; Swamy, K. “A Novel Security Approach for Detecting Black Hole Attack in MANET”, Communications in Computer and Information Science. Vol. 70, pp 217-223.

      [9] Kurosawa, S.; Nakayama, H.; Kato, N.; jamalipour, A.; Nemoto, Y. (2007) “Detecting Blackhole Attack on AODV-based Mobile Ad Hoc Networks by Dynamic Learning Method”, International Journal of Network Security. Vol. 5(3): 338-346, November 2007.

      [10] Ping YI, Ting ZHU, Ning LIU, Yue WU, Jianhua L, (2012) “Cross-layer Detection for Black Hole Attack in Wireless Network”, Journal of Computational Information Systems.

      [11] Rajni Garg, Vikas Mongia (2018), “Mitigation of Black Hole Attack in Mobile Ad-Hoc Network Using Artificial Intelligence Technique”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Volume 3, Issue 1, ISSN: 2456-3307, pages 1168-1174.

      [12] Suman Brar, Mohit Angurala (2017) “Cooperative Black Hole Attack Prevention by Particle Swarm Optimization with Multiple Swarms”, International Journal of Advance Research, Ideas and Innovations in Technology, Volume3, Issue 1, pages 858-863.

      [13] Neelam Janak Kumar Patel, Dr. Khushboo Tripathi (2017) “Trust Value based Algorithm to Identify and Defense GrayHole and Black-Hole attack present in MANET using Clustering Method”, IJSRSET, Volume 4, Issue 4 pages 281-287.

      [14] Rupinder Kaur, Parminder Singh (2014) “Black Hole and Greyhole Attack in Wireless Mesh Network”, American Journal of Engineering Research (AJER), Volume-3, Issue-10, pp-41-47.

      [15] Maryam Motamedi, Nasser Yazdani (2015) “Detection of Black Hole Attack in Wireless Sensor Network Using UAV”, IEEE IKT2015 7th International Conference on Information and Knowledge Technology.

      [16] Ankur mishra, Ranjeet Jaiswal, Sanjay Sharma (2013) “A Novel Approach for Detecting and Eliminating Cooperative Black Hole Attack using Advanced DRI Table in Ad hoc Network”,2013 3rd IEEE International Advance Computing Conference (IACC). Pages 499-504.

      [17] Opinder Singh, J Singh et. al. (2016),” An Intelligent Intrusion Detection and Prevention System for Safeguard Mobile Adhoc Networks against Malicious Nodes”, International Journal of Science & Technology, Vol.10,pp.1-12.

      [18] Satyajayant Misra, Kabi Bhattarai, and Guoliang Xue (2011) “BAMBi: Blackhole Attacks Mitigation with Multiple Base Stations in Wireless Sensor Networks”, IEEE ICC 2011 proceedings.

      [19] Ghathwan K.; Yaakub, A. (2014) An Artificial Intelligence Technique for Prevent Black Hole Attacks in MANET. SCDM 2014, Advances in Intelligent Systems and Computing. Springer International Publishing Switzerland.

      [20] Galeeva G., Aktasheva A. Forecasting the Dynamics of Foreign Direct Investment in the Russian Economy, Astra Salvensis, Supplement No. 2, p. 137, 2017.

      [21] Dashkin R. Determinations of Investment Activity of Russian Companies, Astra Salvensis, Supplement No. 2, p. 397, 2017.

      [22] Gabdrakhmanov N., Ergunova O. Industrial Production Zones as a Tool of Development of the Regional Economy (on the Example of the Republic of Tatarstan and the Sverdlovsk Region), Astra Salvensis, Supplement No. 2, p. 447, 2017..

      [23] Jatinder Singh, Lakhwinder Kaur and Savita Gupta (2010), “A MAC Layer Based Defense Architecture for Reduction-of-Quality (RoQ) Attacks in Wireless LAN”, International Journal of Computer Science and Information Security, Vol. 7, No. 1, pp. 284-291.


 

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Article ID: 23035
 
DOI: 10.14419/ijet.v7i4.7.23035




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