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A Hybrid Approach for Black-hole Intrusion Detection using Fuzzy Logic and PSO Algorithm

  • M. Rohani hajiabadi (Department of Computer Engineering, Information Technology and Electrical, Islamic Azad University) ;
  • S. Gheisari (Department of Computer, Science and Research Branch, Islamic Azad University) ;
  • A. Ahvazi (Department of Electrical & Computer Engineering, Tarbiat Modares University)
  • Received : 2024.10.05
  • Published : 2024.10.30

Abstract

Wireless Sensor Networks (WSN) includes a large number of small sensor nodes and low cost, which are randomly located in a region. The wireless sensor network has attracted much attention from universities and industry around the world over the past decades, with features denser levels of node deployment, self-configuration, uncertainty of sensor nodes, computing, and memory constraints. Black-hole attack is one of the most known attacks on this network. In this study, the combination of fuzzy logic and particle swarm optimization (PSO) algorithms is proposed as an effective method for detecting black-hole attack in the AODV protocol. In the current study, a new function has been proposed in order to determine the membership of fuzzy parameters based on the particle swarm optimization algorithm. The proposed method was evaluated in different scenarios and was compared with other state of arts. The simulation result of this method proved the better performance in both detection rate and delivered packet rate.

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References

  1. Taneja, Sunil and Kush, Ashwani,"A Survey of Routing Protocols in Mobile Ad Hoc Networks", International Journal of Innovation-management and Technology, 2010, Vol.11, No. 3 
  2. Siddiqui, Shadab, Khan, Parvez.Mahmood and Khan, Muhammad.Usman," Fuzzy Logic Based Intruder Detection System in Mobile Adhoc Network ", BIJIT, December, 2014, ISSN 0973-5658, vol.6 No.2. 
  3. Ortiz Antonio.M and Olivares, Teresa," Fuzzy Logic Applied to Decision Making in Wireless Sensor Networks", Fuzzy Logic - Emerging Technologies and Applications, 2012, ISBN 978-953-51-0337-0. 
  4. Kaur, Arvinder, Saibal K Pal, and Amrit Pal Singh. "Hybridization of K-Means and Firefly Algorithm for intrusion detection system." International Journal of System Assurance Engineering and Management:1-10. 
  5. Chahal, Jasmeen K, and Asst Prof Amanjot Kaur. "A Hybrid Approach based on Classification and Clustering for Intrusion Detection System." 2016. 
  6. Chitrakar, Roshan, and Huang Chuanhe. "Anomaly detection using Support Vector Machine classification with k-Medoids clustering." Internet (AH-ICI), 2012 Third Asian Himalayas International Conference on. 
  7. Chitrakar, Roshan, and Chuanhe Huang. "Anomaly based intrusion detection using hybrid learning approach of combining k-medoids clustering and naive Bayes classification." Wireless Communications, Networking and Mobile Computing (WiCOM), 2012 8th International Conference on. 
  8. Syarif, Iwan, Adam Prugel-Bennett, and Gary Wills. "Data mining approaches for network intrusion detection: From dimensionality reduction to misuse and anomaly detection." Journal of Information Technology Review, 2012, 3, (2), 70-83. 
  9. Pandeeswari, N, and Ganesh Kumar. "Anomaly detection system in cloud environment using fuzzy clustering based ANN." Mobile Networks and Applications, 2016, 21, (3), 494-505. 
  10. Sunita, Swain, Badajena J Chandrakanta, and Rout Chinmayee. "A Hybrid Approach of Intrusion Detection using ANN and FCM." European Journal of Advances in Engineering and Technology, 2016, 3 (2), 6-14.
  11. Soheily-Khah, Saeid, Pierre-Francois Marteau, and Nicolas Bechet. "Intrusion detection in network systems through hybrid supervised and unsupervised mining process-a detailed case study on the ISCX benchmark dataset.", 2017. 
  12. Lal, Chunnu and Shrivastava, Akash," An energy preserving detection mechanism for blackhole attack in wireless sensor networks "International Journal of Computer Applications, 2015 - Citeseer.