Keywords:-
Article Content:-
Abstract
Mobile Ad hoc Networks are self-organising, infrastructure-less networks with dynamic topologies. These characteristics provide flexibility in network deployment but also make the network highly susceptible to serious security threats such as blackhole, sybil and wormhole attacks that can affect network performance. Standard ANN has been identified as a strong intrusion detection technique once the best hyperparameters were selected also standard optimization algorithms often suffer from premature convergence during optimization process, this study introduces a Chaotic Tent Map-Enhanced Pelican Optimization Algorithm-Artificial Neural Network (CTM-POA-ANN) framework to improve intrusion detection performance through efficient ANN hyperparameter optimization. The proposed methodology intends to improve population diversity, global exploration and effective fine-tuning of ANN hyperparameters using enhanced pelican optimization algorithm, the CTM-POA-ANN technique is tested using network traffic generated in the NS-3 simulation under normal communication and multiple attack scenarios. The experimental results depict that the proposed CTM-POA-ANN framework achieved 95.93% precision, 95.84% recall, accuracy of 95.85% and F1-score of 95.77% which outperforms traditional ANN and POA-ANN paradigms significantly. The findings indicate that the integration of Chaotic Tent Map with the Pelican Optimization Algorithm significantly enhances ANN hyperparameter optimization, resulting in a robust, efficient, and intelligent intrusion detection framework capable of improving the security and reliability of MANETs under dynamic routing attack scenarios.
References:-
References
Devika B and Sudha, P. N. (2020). Power optimization in MANET using topology Management. Engineering Science and Technology, an International Journal, 23(3), 565–575.
Elif V. and Bilal, A. (2020) "Bird swarm algorithms with chaotic mapping," Artificial Intelligence Review, 53, 1373–1414, 2020
Farahani, G. (2021). Black hole attack detection using K-nearest neighbor algorithm and reputation calculation in mobile ad hoc networks. Security and Communication Networks, 2021(1), 1-15.
https://onlinelibrary.wiley.com/doi/epdf/10.1155/2021/8814141
Hassan, M. H., Mostafa, S. A., Mahdin, H., Mustapha, A., Ramli, A. A., Hassan, M. H. and Jubair, M. A. (2021). Mobile ad-hoc network routing protocols of time-critical events for search and rescue missions. Bulletin of Electrical Engineering and Informatics, 10(1), 192–199.
https://mail.beei.org/index.php/EEI/article/download/2506/1885
Majid, M., Habib, S., Javed, A. R., Rizwan, M., Srivastava, G., Gadekallu, T. R. and Lin, J. C. W. (2022). Applications of wireless sensor networks and internet of things frameworks in the industry revolution 4.0: A systematic literature review. Sensors, 22(6), 1-36, https:// www.mdpi.com/1424-8220/22/6/2087/pdf
Nazib, A. and Moh, S. (2021). Reinforcement learning-based routing protocols for vehicular ad hoc networks: A comparative survey. IEEE Access, 9,
–27587. https://ieeexplore. ieee.org/stamp/stamp.jsp?tp=&arnumber=9351930
Ojo, O. S., Oyediran, M. O., Olagunju, K. M., Opoola, K. B., Ogbonnia, E. O. and Adebiyi, A. A. (2025). A hybrid chaotic tent map-pelican optimization algorithm for software defect prediction. NIPES Journal of Science and Technology Research, 7(Special Issue: Landmark University International Conference SEB4SDG 2025), 2386–2394.
https://doi.org/10.37933/nipes/7.4.2025.SI282
Rahman, M. T., Alauddin, M., Dey, U. K. and Sadi, S. (2023). Adaptive, secure and efficient routing protocol to enhance the performance of Mobile Ad Hoc Network (MANET). Applied Computer Science, 19(3), 133–159. https://yadda.icm.edu.pl/baztech /element /bwmeta1.element.baztech-0ffffd6a-7852-4fbd-9d88-53969a2882d3/c/jszabelski_9.pdf
Rani, P., Kavita, Nhunguyen, G. and Verma, S. (2020). Mitigation of black hole and gray hole attack using swarm inspired algorithm with artificial neural network. IEEE Access, 8, 121755-121764. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9125902
Ravi N. and Ramachandran G. A (2020). robust intrusion detection system using machine learning techniques for MANET. International Journal of Knowledge-Based and Intelligent Engineering Systems, 24(3), 253-260. doi:10.3233/KES-200047
Rezwan, S. and Choi, W. (2021). A survey on applications of reinforcement learning in flying ad-hoc networks. Electronics, 10(4), 1-19.
https://www.mdpi.com/2079-9292/10/4/449/pdf?version=1614067110
Saravanan, S., Dar, S. A., Rather, A. A., Qayoom, D. and Ali, I. (2025). Deep learning models for intrusion detection systems in MANETs: A comparative analysis. Decision Making Advances, 3(1), 96–110. https://www.dmajournal.org/index.php/dema/article/view/56/51
Sayan, M. and Debika, N. (2022). Regression Model Estimation Using Least Absolute Deviations, Least Squares Deviations and Minimax Absolute Deviations Criteria. International Journal of Research and Analytical Reviews (IJRAR), 9(1),
–193. https://journalsweb.org/siteadmin/upload/P1215019.pdf
Shah, N. El-Ocla, H. and Shah, P. (2022). Adaptive routing protocol in mobile ad-hoc networks using genetic algorithm. IEEE Access, 10, 132949–132964. https://ieeexplore.ieee.org/ stamp/stamp.jsp?tp=&arnumber=9996161
Shami, T. M., El-Saleh, A. A., Alswaitti, M., Al-Tashi, Q., Summakieh, M. A. and Mirjalili, S. (2022). Particle swarm optimization: A comprehensive survey. IEEE, 10, 10031-10061.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9680690
Sultan, M. T., Sayed, H. El and Khan, M. A. (2023). An intrusion detection mechanism for MANETs based on deep learning artificial neural networks. International Journal of Computer Networks and Communications, 15(1), 1-15
https://aircconline.com/ijcnc /V15N1/15123cnc01.pdf
Torky, M., El-Dosuky, M., Goda, E., Snášel, V., and Hassanien, A. E. (2022). Scheduling and securing drone charging system using particle swarm optimization and blockchain technology. Drones, 6(9), 1–26.
https://www.mdpi.com/2504446X/6/9/237/pdf?version=1662524781
Trojovský, P. and Dehghani, M. (2022). Pelican optimization algorithm: A novel nature-inspired algorithm for engineering applications. Sensors, 22(3), 1-34
https://www.mdpi.com /1424 -8220/22/3/855/pdf
Wang, L. and Cheng H. (2019). Pseudo-Random Number Generator Based on Logistic Chaotic System," Entropy, 21(10), 960-971.
Zaimoğlu, E. A., Yurtay, N., Demirci, H., & Yurtay, Y. (2023). A binary chaotic horse herd optimization algorithm for feature selection. Engineering Science and Technology, an International Journal, 44.