International Journal of Computer Networks and Applications

Open Access Open Access  Restricted Access Subscription Access

ANFIS-RSOA Approach for Detecting and Preventing Network Layer Attacks in MANET


Affiliations
1 Department of Computer Science and Engineering, Vels Institute of Science, Technology & Advance Studies (VISTAS), Chennai, Tamil Nadu, India
2 Department of Data Science and Business Systems, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India
 

The primary obstacle typically encountered in Mobile Ad hoc Networks (MANETs) pertains for mitigating the impact of attacks prompted through malevolent nodes or identify promptly as well as addressing the certain nodes presence. This paper presents a hybrid technique in assault detection present in the context of MANETs. The research work focuses on addressing the challenge present in the MANET by introducing the robust Intrusion Detection System (IDS) using hybrid Machine Learning (ML) methods. The proposed approach for identifying attacks involves the utilization of the Adaptive Neuro Fuzzy Inference System in conjunction with the Rat Swarm Optimization Algorithm (ANFIS-RSOA). Hence, this hybrid ML approach has capability to produce high secure with precise and reliable outcomes. The suggested protocols concentrate on the security within a network by effectively identifying and mitigating potential assaults. The suggested methodology is executed within the NS2 platform and afterwards compared to various conventional methodologies, namely (PSO) Particle Swarm Optimization, (WOA), Whale Optimization Algorithm and Grey Wolf Optimization Algorithm (GWO). In order to evaluate the efficacy of the suggested methodology, it is subjected to testing using two distinct types of attacks, namely the (BHA) Black Hole Attack and the Wormhole Attack (WHA). This proposed ANFIS-RSOA method performance metrics such as jitter, throughput, delay, Packet Delivery Ratio (PDR), and low end-to-end delay is evaluated and compared with existing IDS methods. Moreover, the purpose of study is to protect both individual network nodes and their connections to one another.

Keywords

BHA, WHA, ANFIS, Rat Swarm Optimization A1gorithm, Delivery Ratio, GWO.
User
Notifications
Font Size

  • Zulfiqar Ali Zardari, Kamran Ali Memon, Reehan Ali Shah, Sanaullah Dehraj, Iftikhar Ahmed, “A lightweight technique for detection and prevention of wormhole attack in MANET”, EAI Endorsed Transactions, Scalable Information Systems, Vol.8, No. 29, 2021

  • Safaa LAQTIB, Khalid El YASSINI and Moulay Lahcen HASNAOUI, “A Deep Learning Methods for Intrusion Detection Systems based Machine Learning in MANET”, IJECE, Vol 10, No. 3, June 2020, pp 2701-2709

  • Alka Chaudhary, V.N. Tiwari and Anil Kumar, “Design an anomaly-based intrusion detection system using soft computing for mobile ad hoc networks”, Int. J. Soft Computing and Networking, Vol. 1, No. 1, 2016 – 17

  • Udaya Kumar Addanki, B. Hemantha Kumar, “Enhancement OLSR Routing Protocol using Particle Swarm Optimization (PSO) and Genrtic Algorithm (GA) in MANETS”, IJCSNS International Journal of Computer Science and Network Security, VOL.22, No.4, April 2022

  • Kumaravel, A., Chandrasekaran, M. Performance analysis of malicious node detection in MANET using ANFIS classification approach. Cluster Comput 22 (Suppl 6), 13445–13452 (2019). https://doi.org/10.1007/s10586-018-1955-z

  • Mukul Shukla, Brijendra Kumar Joshi, Upendra Singh, “Mitigate Wormhole Attack and Blackhole Attack Using Elliptic Curve Cryptography in MANET”, Wireless Personal Communications (2021) 121:503–526

  • Shalini Jain, Satbir Jain, “Detection and prevention of wormhole attack in mobile adhoc networks”, International Journal of Computer Theory and Engineering, Vol. 2, No. 1 February, 2010

  • Jaspal Kumar, M. Kulkarni, Daya Gupta, “Effect of Black Hole Attack on MANET Routing Protocols”, I. J. Computer Network and Information Security, 2013, 5, 64-72.

  • K Srinivas, V Harsha Shastri, Vinay Kumar Nassa, Gudapati Syam Prasad and Prathipati Ratna Kumar, “Discernment and Diminution of Black Hole Attack in Mobile Ad-Hoc Network using Artificial Intelligence”, 2021 doi:10.1088/1742-6596/2040/1/012037

  • Ausaf Umar Khan, Milind Madhukar Mushrif, Manish Devendra Chawhan, Bhumika Neole, “Performance Analysis of Adhoc On-demand Distance Vector Protocol under the influence of black-Hole, Gray-Hole and Worm-Hole Attacks in Mobile Adhoc Network”, Proceedings of the Fifth International Conference on Intelligent Computing and Control Systems (ICICCS 2021)

  • Moudni, H., Er-rouidi, M., Mouncif, H., & Hadadi, B. E. (2019). Black Hole attack Detection using Fuzzy based Intrusion Detection Systems in MANET. Procedia Computer Science, 151, 1176–1181. doi:10.1016/j.procs.2019.04.168

  • Amouri, A.; Alaparthy, V.T.; Morgera, S.D. A Machine Learning Based Intrusion Detection System for Mobile Internet of Things. Sensors 2020, 20, 461. https://doi.org/10.3390/s20020461

  • G. Dhiman, M. Garg, A. Nagar, V. Kumar and M. Dehghani, "A Novel Algorithm for Global Optimization: Rat Swarm Optimizer," Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 8, pp. 8457–8482, 2021.

  • John Felix Charles Joseph, Amitabha Das , Bu-Sung Lee , Boon-Chong Seet, “CARRADS: Cross layer based adaptive real-time routing attack detection system for MANETS”, Computer Networks 54 (2010) 1126–1141

  • Aniruddha Bhattacharyya Arnab Banerjee Dipayan Bose, “Different types of attacks in Mobile ADHOC Network: Prevention and mitigation techniques”, april 20, 2022

  • Mohammad Al-Shurman, Seong-Moo Yoo and Seungjin Park, “Black Hole Attack in Mobile Ad Hoc Networks”, 2014 DoI:10.1145/986537.986560.

  • Houda Moudni, Mohamed Er-rouidi, Hicham Mouncif and Benachir ElHadadi, “Black Hole attack Detection using Fuzzy based Intrusion Detection Systems in MANET”, Procedia Computer Science, Vol. 151, pp. 1176-1181, 2019

  • Muhannad Tahboush and Mary Agoyi, “A Hybrid Wormhole Attack Detection in Mobile Ad-Hoc Network (MANET)”, IEEE Access, Vol.9, 2021

  • Prabhakar Reddy, Bhaskar Reddy, Dhananjaya, “The AODV routing protocol with built-in security to counter blackhole attack in MANET”, Materials Today: Proceedings, Vol. 50, Part 5, pp. 1152-1158, 2022

  • T.J.Nagalakshmi, A.K.Gnanasekar, G.Ramkumara A.Sabarivani, “Machine learning models to detect the blackhole attack in wireless adhoc network”, Materials Today: Proceedings, Vol. 47, Part 1, 2021, pp. 235-239

  • Hikal Noha A., Shams Mahmoud Y., Salem Hanaac, Eid Marwa M., "Detection of black-hole attacks in MANET using adaboost support vector machine", Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 669-682, 2021

  • Van-Hau Nguyen, Vi Hoai Nam, Linh Manh Dao, Quy Vu Khanh, “An Improved Agent-Based AODV Routing Protocol for MANET”, June 2021Industrial Networks and Intelligent Systems 8(27):1-8, 2021

  • Abdul Majid Soomro, Mohd Farhan Bin Fudzee, Muzamil Hussain and 3Hafiz Muhammad Saim, “A Hybrid Routing Approach Comparison with AODV Protocol Regarding Speed for Disaster Management in MANET”, Journal of Computer Science, Vol. 18, No.3, pp. 204-213, 2022.

  • Xiaojuan Ran, Xiangbing Zhou , Mu Lei , Worawit Tepsan and Wu Deng , “A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots”, Appl. Sci., 11, 2021.

  • Fouad H. Awad and Murtadha M. Hamad, “Improved k-Means Clustering Algorithm for Big Data Based on Distributed Smartphone Neural Engine Processor”, Electronics 2022, 11, 2022

  • Leticia Amador-Angulo, Oscar Castillo, Cinthia Peraza and Patricia Ochoa, “An Efficient Chicken Search Optimization Algorithm for the Optimal Design of Fuzzy Controllers”, 2021.

  • Zhenwu Wang, Chao Qin, Benting Wan, William Wei Song, and Guoqiang Yang, “ An Adaptive Fuzzy Chicken Swarm Optimization Algorithm”, Mathematical Problems in Engineering, 2021. [28] Ramesh Kumar Selvaraju, Ganapathy Somaskandan, “ACS algorithm tuned ANFIS-based controller for LFC in deregulated environment”, Journal of applied research and technology, Vol.15, No.2, 2017

  • Issam Griche, Messalti Sabir, Kamel Saoudi, Yaakoub Mohamed Touafek, “A New Adaptive Neuro-Fuzzy Inference System (ANFIS) and PI Controller to Voltage Regulation of Power System Equipped by Wind Turbine”, European Journal of Electrical Engineering 21(2):149-155, 2019.

  • Ali Toolabi Moghadam , Srete Nikolovski, Mahdiyeh Eslami, Shima Rashidi , Morteza Aghahadi and Behdad Arandian , “Adaptive Rat Swarm Optimization for Optimum Tuning of SVC and PSS in a Power System”, International Transactions on Electrical Energy Systems / 2022.

  • Gaurav Dhiman, Meenakshi Garg, Atulya Nagar, Vijay Kumar and Mohammad Dehghani, “A novel algorithm for global optimization: Rat Swarm Optimizer”, Journal of Ambient Intelligence and Humanized Computing, Vol. 12, pp. 8457–8482, 2021.


Abstract Views: 155

PDF Views: 1




  • ANFIS-RSOA Approach for Detecting and Preventing Network Layer Attacks in MANET

Abstract Views: 155  |  PDF Views: 1

Authors

Sivanesan N.
Department of Computer Science and Engineering, Vels Institute of Science, Technology & Advance Studies (VISTAS), Chennai, Tamil Nadu, India
Rajesh A.
Department of Computer Science and Engineering, Vels Institute of Science, Technology & Advance Studies (VISTAS), Chennai, Tamil Nadu, India
K. S. Archana
Department of Data Science and Business Systems, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India

Abstract


The primary obstacle typically encountered in Mobile Ad hoc Networks (MANETs) pertains for mitigating the impact of attacks prompted through malevolent nodes or identify promptly as well as addressing the certain nodes presence. This paper presents a hybrid technique in assault detection present in the context of MANETs. The research work focuses on addressing the challenge present in the MANET by introducing the robust Intrusion Detection System (IDS) using hybrid Machine Learning (ML) methods. The proposed approach for identifying attacks involves the utilization of the Adaptive Neuro Fuzzy Inference System in conjunction with the Rat Swarm Optimization Algorithm (ANFIS-RSOA). Hence, this hybrid ML approach has capability to produce high secure with precise and reliable outcomes. The suggested protocols concentrate on the security within a network by effectively identifying and mitigating potential assaults. The suggested methodology is executed within the NS2 platform and afterwards compared to various conventional methodologies, namely (PSO) Particle Swarm Optimization, (WOA), Whale Optimization Algorithm and Grey Wolf Optimization Algorithm (GWO). In order to evaluate the efficacy of the suggested methodology, it is subjected to testing using two distinct types of attacks, namely the (BHA) Black Hole Attack and the Wormhole Attack (WHA). This proposed ANFIS-RSOA method performance metrics such as jitter, throughput, delay, Packet Delivery Ratio (PDR), and low end-to-end delay is evaluated and compared with existing IDS methods. Moreover, the purpose of study is to protect both individual network nodes and their connections to one another.

Keywords


BHA, WHA, ANFIS, Rat Swarm Optimization A1gorithm, Delivery Ratio, GWO.

References





DOI: https://doi.org/10.22247/ijcna%2F2023%2F223693