Detection System using Random X-Layer Mobility with QCH Algorithm in Wireless Ad-hoc Networks
| International Journal of Electronics and Communication Engineering |
| © 2023 by SSRG - IJECE Journal |
| Volume 10 Issue 7 |
| Year of Publication : 2023 |
| Authors : S. Sandosh, P. Saravanan, D. Shofia Priyadharshini, G. Anitha |
10.14445/23488549/IJECE-V10I7P101
How to Cite?
S. Sandosh, P. Saravanan, D. Shofia Priyadharshini, G. Anitha, "Detection System using Random X-Layer Mobility with QCH Algorithm in Wireless Ad-hoc Networks," SSRG International Journal of Electronics and Communication Engineering, vol. 10, no. 7, pp. 1-12, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I7P101
Abstract:
Intrusion Detection Systems (IDS) are critical in identifying malicious activities that degrade network performance. An ad hoc system is a self-organizing, transient network with no infrastructure. Because of its open medium, constantly shifting topologies, co - operative protocols, loss of centralised monitoring and administration point, and absence of a distinct line of protection, wireless ad-hoc networks are especially susceptible. Many intrusion detection methods built for fixed wired networks are no longer relevant in this new context. Then, we present the novel IDS and response techniques we are working on for wireless ad hoc networks (WANet). This research presents two IDS techniques. Using the permissive mode in line with the location of the nodes throughout the simulation is the first technique. Within the AODV Routing protocol context, this method is known as the quasi-cluster head (QCH) algorithm. The given simulation area is segmented into four quadrants, each having a circular inside the centre. Each node will be able to collect data via neighbours within the radio transmission range. X-layer IDS with Random X-Layer Mobility is the second technique. We are developing tools to identify ad-hoc basis flooding, routing disruption, and dropping attacks against WANet. On simulation model networks, the effectiveness of evolved programmes is evaluated.
Keywords:
Intrusion detection systems, QCH algorithm, AODV, X-layer IDS, WANet, Random X-layer mobility.
References:
[1] Ayoub Alsarhan et al., “Machine Learning-Driven Optimization for SVM-Based Intrusion Detection System in Vehicular Ad Hoc Networks,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, pp. 6113-6122, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] S. Sivanesh, and V. R. Sarma Dhulipala, “Accurate and Cognitive Intrusion Detection System (ACIDS): A Novel Black Hole Detection Mechanism in Mobile Ad Hoc Networks,” Mobile Networks and Applications, vol. 26, no. 4, pp. 1696-1704, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Zainab Ali Abbood et al., “A Survey on Intrusion Detection System in Ad Hoc Networks Based on Machine Learning,” 2021 International Conference of Modern Trends in Information and Communication Technology Industry (MTICTI), pp. 1-8, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Saurabh Singh et al., “Intrusion Detection System-Based Security Mechanism for Vehicular Ad-Hoc Networks for Industrial IoT,” IEEE Consumer Electronics Magazine, vol. 11, no. 6, pp. 83-92, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Erfan A. Shams, and Ahmet Rizaner, “A Novel Support Vector Machine Based Intrusion Detection System for Mobile Ad Hoc Networks,” Wireless Networks, vol. 24, no. 5, pp. 1821-1829, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[6] R. Srilakshmi, and Jayabhaskar Muthukuru, “Intrusion Detection in Mobile Ad-Hoc Network using Hybrid Reactive Search and Bat Algorithm,” International Journal of Intelligent Unmanned Systems, vol. 10, no. 1, pp. 65-85, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Erfan A. Shams, Ahmet Rizaner, and Ali Hakan Ulusoy, “Trust Aware Support Vector Machine Intrusion Detection and Prevention System in Vehicular Ad Hoc Networks,” Computers & Security, vol. 78, pp. 245-254, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[8] R. Santhana Krishnan et al., “Modified Zone Based Intrusion Detection System for Security Enhancement in Mobile Ad Hoc Networks,” Wireless Networks, vol. 26, no. 2, pp. 1275-1289, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Satyanarayana Pamarthi, and R. Narmadha, “Literature Review on Network Security in Wireless Mobile Ad-Hoc Network for IoT Applications: Network Attacks and Detection Mechanisms,” International Journal of Intelligent Unmanned Systems, vol. 10, no. 4, pp. 482-506, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Shivam Kumawat, Harneet Kaur, and Omdev Dahiya, “An Analytical Study on Intrusion Detection System in Integrated Vehicular AdHoc Network Attacks,” In 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM), pp. 378-383, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Junwei Liang et al., “A Novel Intrusion Detection System for Vehicular Ad Hoc Networks (VANETs) Based on differences of Traffic Flow and Position,” Applied Soft Computing, vol. 75, pp. 712-727, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Rajendra Prasad P, and Shiva Shankar, “Secure Intrusion Detection System Routing Protocol for Mobile Ad‐Hoc Network,” Global Transitions Proceedings, vol. 3, no. 2, pp. 399-411, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Bandecchi Susan, and Dascalu Nicoleta, “Intrusion Detection Scheme in Secure Zone Based System,” Journal of Computing and Natural Science, vol. 1, no. 1, pp. 19-25, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Man Zhou et al., “Distributed Collaborative Intrusion Detection System for Vehicular Ad Hoc Networks Based on Invariant,” Computer Networks, vol. 172, p. 107174, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Masoud Abdan, and Seyed Amin Hosseini Seno, “Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad Hoc Network (MANET),” Wireless Communications and Mobile Computing, vol. 2022, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Mahendra Prasad, Sachin Tripathi, and Keshav Dahal, “A Probability Estimation-Based Feature Reduction and Bayesian Rough Set Approach for Intrusion Detection in Mobile Ad-Hoc Network,” Applied Intelligence, vol. 53, pp. 7169-7185, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Vasaki Ponnusamy et al., “Intrusion Detection Systems in Internet of Things and Mobile Ad-Hoc Networks,” Computer Systems Science and Engineering, vol. 40, no. 3, pp. 1199-1215, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Jose Vicente Sorribes et al., “Energy-Aware Randomized Neighbor Discovery Protocol Based on Collision Detection in Wireless Ad Hoc Networks,” Mobile Networks and Applications, pp. 1-18, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Kulkarni Sagar S, and Kahate Sandip A, “Review of A Semantic Approach to Host-based Intrusion Detection Systems using Contiguous and Dis-contiguous System Call Patterns,” SSRG International Journal of Computer Science and Engineering, vol. 2, no. 6, pp. 9-12, 2015.
[Publisher Link]
[20] Arunkumar Rajendran, Nagaraj Balakrishnan, and Ajay P, “Deep Embedded Median Clustering for Routing Misbehaviour and Attacks Detection in Ad-Hoc Networks,” Ad Hoc Networks, vol. 126, p. 102757, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Naveen Chakravarthy Sattaru et al., “Evaluation of Cluster Approach for Detecting Black Hole Attacks in Wireless Ad Hoc Networks using Deep Learning,” In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 821-825, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] C. Murugesh, and S. Murugan, “Moth Search Optimizer with Deep Learning Enabled Intrusion Detection System in Wireless Sensor Networks,” SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 4, pp. 77-90, 2023.
[CrossRef] [Publisher Link]
[23] Jose Vicente Sorribes, Jaime Lloret, and Lourdes Peñalver, “Analytical Models for Randomized Neighbor Discovery Protocols Based on Collision Detection in Wireless Ad Hoc Networks,” Ad Hoc Networks, vol. 126, p. 102739, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Leonid Legashev, and Luybov Grishina, “Development of an Intrusion Detection System Prototype in Mobile Ad Hoc Networks Based on Machine Learning Methods,” In 2022 International Russian Automation Conference (RusAutoCon), pp. 171-175, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[25] S. Kavitha, “Detecting Network Intrusion Based on Machine Learning Algorithms,” International Journal of P2P Network Trends and Technology, vol. 10, no. 3, pp. 1-5, 2020.
[Publisher Link]
[26] Uppalapati Srilakshmi et al., “A Secure Optimization Routing Algorithm for Mobile Ad Hoc Networks, IEEE Access, vol. 10, pp. 14260-14269, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Amit Chougule et al., “Multibranch Reconstruction Error (MbRE) Intrusion Detection Architecture for Intelligent Edge-Based Policing in Vehicular Ad-Hoc Networks,” IEEE Transactions on Intelligent Transportation Systems, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[28] M. Azath, and Vaishali Singh, “Optimized Convolutional Neural Network Based Privacy Based Collaborative Intrusion Detection System for Vehicular Ad Hoc Network,” SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 2, pp. 143- 156, 2023.
[CrossRef] [Publisher Link]
[29] B. Murugeshwari et al., “Hybrid Key Authentication Scheme for Privacy over Adhoc Communication,” International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 18-26, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[30] S. Kanthimathi, and P. Jhansi Rani, “An Efficient Packet Dropping Attack Detection Mechanism in Wireless Ad-Hoc Networks using ECC Based AODV-ACO Protocol,” Wireless Networks, pp. 1-13, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Adrian P Lauf, Richard A Peters, and William H Robinson, “A Distributed Intrusion Detection System for Resource-Constrained Devices in Ad-Hoc Networks,” Ad Hoc Networks, vol. 8, no. 3, pp. 253-266, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Ming-Yang Su, “Prevention of Selective Black Hole Attacks on Mobile Ad Hoc Networks through Intrusion Detection Systems,” Computer Communications, vol. 34, no. 1, pp. 107-117, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Shivam Kumawat, Harneet Kaur, and Omdev Dahiya, “An Analytical Study on Intrusion Detection System in Integrated Vehicular AdHoc Network Attacks,” In 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM), pp. 378-383, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Mahdi Sadeghizadeh, “A Lightweight Intrusion Detection System Based on RSSI for Sybil Attack Detection in Wireless Sensor Networks,” International Journal of Nonlinear Analysis and Applications, vol. 13, no. 1, pp. 305-320, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Mahendra Prasad, Sachin Tripathi, and Keshav Dahal, “An Enhanced Detection System Against Routing Attacks in Mobile Ad-Hoc Network,” Wireless Networks, vol. 28, no. 4, pp. 1411-1428, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Hadi Otrok et al., “A Game-Theoretic Intrusion Detection Model for Mobile Ad Hoc Networks,” Computer Communications, vol. 31, no. 4, pp. 708-721, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[37] S. Navya Sai, and K. Kishore Raju, “Improved Privacy Preserving Decision Tree Approach for Network Intrusion Detection,” International Journal of Computer & Organization Trends (IJCOT), vol. 6, no. 1, pp. 55-60, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Ningrinla Marchang, and Raja Datta, “Collaborative Techniques for Intrusion Detection in Mobile Ad-Hoc Networks,” Ad Hoc Networks, vol. 6, no. 4, pp. 508-523, 2008.
[CrossRef] [Google Scholar] [Publisher Link]