Proficient Red Deer Optimization-based Relevance Vector Machine (PRDO-RVM) for Elevated Intrusion Detection System in MANET
Proficient Red Deer Optimization-based Relevance Vector Machine (PRDO-RVM) for Elevated Intrusion Detection System in MANET |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-12 |
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| Year of Publication : 2024 | ||
| Author : M. Sasikumar, K. Rohini |
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| DOI : 10.14445/22315381/IJETT-V72I12P128 | ||
How to Cite?
M. Sasikumar, K. Rohini, "Proficient Red Deer Optimization-based Relevance Vector Machine (PRDO-RVM) for Elevated Intrusion Detection System in MANET," International Journal of Engineering Trends and Technology, vol. 72, no. 12, pp. 330-348, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I12P128
Abstract
Mobile Ad-hoc Networks (MANETs) are wireless networks composed of autonomous mobile devices that communicate with each other without relying on a centralized infrastructure. MANET security issues can compromise data confidentiality, integrity, and availability, highlighting the critical need for robust security mechanisms. Intrusion Detection Systems (I.D.S.) are crucial in identifying and mitigating security threats in MANETs. Designing effective I.D.S. for MANETs is inherently challenging due to these networks’ dynamic and resource-constrained nature. To address these challenges, this research proposes the Proficient Red Deer Optimization-based Relevance Vector Machine (PRDO-RVM) for intrusion detection in MANETs. PRDO-RVM leverages the sparsity-inducing properties of Relevance Vector Machine (RVM) and the efficient optimization capabilities of Red Deer Optimization to achieve accurate and efficient intrusion detection in dynamic network environments. By effectively identifying and classifying intrusions, PRDO-RVM enhances the security posture of MANETs, mitigating the risks posed by malicious actors and ensuring the integrity and availability of network communications. Using the NSK-KDD dataset, PRDO-RVM is evaluated for its effectiveness in detecting intrusions in MANETs. The results demonstrate the superior classification accuracy and efficiency of PRDO-RVM compared to existing I.D.S. solutions, affirming its potential as a reliable and scalable security mechanism for MANETs.
Keywords
Intrusion, MANET, Optimization, Security, Krill Herd, Random forest.
References
[1] Quy Khanh Vu, Nam Vi Hoai, and Linh Dao Manh, “A Survey of State-of-the-Art Energy Efficiency Routing Protocols for MANET,” International Journal of Interactive Mobile Technologies, vol. 14, no. 9, pp. 215-226, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Raed Alsaqour et al., “Genetic Algorithm Routing Protocol for Mobile Ad Hoc Network,” Computers, Materials & Continua, vol. 68, no. 1, pp. 941-960, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Lavanya Poluboyina et al., “Multimedia Traffic Transmission Using MAODV and M-MAODV Routing Protocols Over Mobile Ad-hoc Networks,” International Journal of Computer Network and Information Security, vol. 14, no. 3, pp. 47-62, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Faik Kerem Örs, and Albert Levi, “Data Driven Intrusion Detection for 6LoWPAN Based IoT Systems,” Ad Hoc Networks, vol. 143, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] R. Thanuja, and A. Umamakeswari, “Black Hole Detection Using Evolutionary Algorithm for IDS/IPS in MANETs,” Cluster Computing, vol. 22, pp. 3131-3143, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] S. Sankara Narayanan, K. Chidambarathanu, and L.C. Meena, “PFR Based Technique to Detect Intruder in MANET,” Journal of Advanced Research in Dynamical and Control Systems, vol. 12, no. 2 pp. 597-601, 2020.
[Publisher Link]
[7] N. Rajendran, P.K. Jawahar, and R. Priyadarshini, “Makespan of Routing and Security in Cross Centric Intrusion Detection System (CCIDS) Over Black Hole Attacks and Rushing Attacks in MANET,” International Journal of Intelligent Unmanned Systems, vol. 7, no. 4, pp. 162-176, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Nisha Soms, and P. Malathi, “Secured and Anonymous Data Transmission in Manet Environment Using Zone-Based Intrusion Detection System,” Concurrency and Computation Practice and Experience, vol. 31, no. 12, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] N. Rajendran, P.K. Jawahar, and R. Priyadarshini, “Cross Centric Intrusion Detection System for Secure Routing Over Black Hole Attacks in MANETs,” Computer Communications, vol. 148, pp. 129-135, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Shalini Subramani, and M. Selvi, “Multi-Objective PSO Based Feature Selection for Intrusion Detection in IOT Based Wireless Sensor Networks,” Optik, vol. 273, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Gustavo de Carvalho Bertoli et al., “Generalizing Intrusion Detection for Heterogeneous Networks: A Stacked-Unsupervised Federated Learning Approach,” Computers and Security, vol. 127, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Durgesh Srivastava et al., “A Framework for Detection of Cyber Attacks by The Classification of Intrusion Detection Datasets,” Microprocessors and Microsystems, vol. 105, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Hossein Asgharzadeh et al., “Anomaly-Based Intrusion Detection System in The Internet of Things Using a Convolutional Neural Network and Multi-Objective Enhanced Capuchin Search Algorithm,” Journal of Parallel and Distributed Computing, vol. 175, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Judy Simon et al., “Hybrid Intrusion Detection System for Wireless IOT Networks Using Deep Learning Algorithm,” Computers and Electrical Engineering, vol. 102, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Vikash Kumar, and Ditipriya Sinha, “Synthetic Attack Data Generation Model Applying Generative Adversarial Network for Intrusion Detection,” Computers and Security, vol. 125, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] F. Folino et al., “On Learning Effective Ensembles of Deep Neural Networks for Intrusion Detection,” Information Fusion, vol. 72, pp. 48-69, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Murad Ali Khan et al., “An Optimized Ensemble Prediction Model Using Automl Based on Soft Voting Classifier for Network Intrusion Detection,” Journal of Network and Computer Applications, vol. 212, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Thippa Reddy Gadekallu et al., “Moth-Flame Optimization Based Ensemble Classification for Intrusion Detection in Intelligent Transport System for Smart Cities,” Microprocessors and Microsystems, vol. 103, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Xu Zhao et al., “Task Offloading of Cooperative Intrusion Detection System Based on Deep Q Network in Mobile Edge Computing,” Expert Systems with Applications, vol. 206, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Liu Zhiqiang et al., “Intrusion Detection in Wireless Sensor Network Using Enhanced Empirical Based Component Analysis,” Future Generation Computer Systems, vol. 135, pp. 181-193, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Hind Bangui, Mouzhi Ge, and Barbora Buhnova, “A Hybrid Data-Driven Model for Intrusion Detection in VANET,” Procedia Computer Science, vol. 184, pp. 516-523, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[22] I. Benmessahel, K. Xie, M. Chellal, and T. Semong, “A New Evolutionary Neural Networks Based on Intrusion Detection Systems Using Locust Swarm Optimization,” Evolutionary Intelligence, vol. 12, no. 2, pp. 131–146, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Waheed Ali H. M. Ghanem et al., “An Efficient Intrusion Detection Model Based on Hybridization of Artificial Bee Colony and Dragonfly Algorithms for Training Multilayer Perceptrons,” IEEE Access, vol. 8, pp. 130452-130475, 2020.
[CrossRef] [Google Scholar] [Publisher Link]