MANET Intrusion Detection Model Based on Dynamic Lyrebird Optimization with Hybrid Convolutional Neural Network and Long Short-Term Memory

Divya Boya; Syed Shabbeer Ahmad

doi:https://doi.org/10.14445/22315381/IJETT-V73I11P124

Research Article | Open Access | Download PDF

Volume 73 | Issue 11 | Year 2025 | Article Id. IJETT-V73I11P124 | DOI : https://doi.org/10.14445/22315381/IJETT-V73I11P124

MANET Intrusion Detection Model Based on Dynamic Lyrebird Optimization with Hybrid Convolutional Neural Network and Long Short-Term Memory

Divya Boya, Syed Shabbeer Ahmad

Received	Revised	Accepted	Published
17 Sep 2025	12 Nov 2025	15 Nov 2025	25 Nov 2025

Citation :

Divya Boya, Syed Shabbeer Ahmad, "MANET Intrusion Detection Model Based on Dynamic Lyrebird Optimization with Hybrid Convolutional Neural Network and Long Short-Term Memory," International Journal of Engineering Trends and Technology (IJETT), vol. 73, no. 11, pp. 336-348, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I11P124

Abstract

Wireless portable nodes with a decentralized and distributed network form a Mobile Ad hoc Network (MANET) that directly links without any fixed centralized administration or communication base station. Continuously moving the MANET nodes in arbitrary and random directions leads to difficulties, such as security threats in networks. Node energy and mobility are constantly changing due to node movements and the resulting changes in topology and direction. The topmost challenges in MANET are energy consumption and security. Compared to the previous routing protocols, the Optimization methods are more efficient for Cluster Head (CH) selection because they provide optimal solutions to address the issues. Due to the mobility of nodes in MANET, several issues have been raised, including sudden changes, reliability, security, power consumption, and path maintenance. To overcome these challenges, this work presents a novel, optimized deep learning model. The clusters are formed using Spectral Clustering (SC), followed by the CH selected using Dynamic Lyrebird Optimization (DLO) for optimal routing and energy consumption in MANET. Different intrusions are detected by using Sailfish Optimization (SO) with a Hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) model. The varying network densities and different parameters, along with the NS-3 Tools, are used to conduct extensive simulations. The proposed work demonstrates improvements. In intrusion detection, it extends network lifespan and balances energy consumption when compared to existing state-of-the-art models.

Keywords

Spectral clustering, MANET, Dynamic lyrebird optimization, Sailfish optimization, and Hybrid CNN-LSTM.

References

[1] Zhao Niu et al., “Identification of Critical Nodes for Enhanced Network Defense in Manet-IoT Networks,” IEEE Access, vol. 8, pp. 183571-183582, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Cheng-Xiang Wang et al., “Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges,” IEEE Wireless Communications, vol. 27, no. 1, pp. 16-23, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Bin Liao et al., “Security Analysis of IoT Devices by using Mobile Computing: A Systematic Literature Review,” IEEE Access, vol. 8, pp. 120331-120350, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Amin Shahraki et al., “A Survey and Future Directions on Clustering: from WSNs to IoT and Modern Networking Paradigms,” IEEE Transactions on Network and Service Management, vol. 18, no. 2, pp. 2242-2274, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Daniele Bringhenti et al., “Automated Firewall Configuration in Virtual Networks,” IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 2, pp. 1559-1576, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Inadyuti Dutt, Samarjeet Borah, and Indra Kanta Maitra, “Immune System Based Intrusion Detection System (IS-IDS): A Proposed Model,” IEEE Access, vol. 8, pp. 34929-34941, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Ziadoon Kamil Maseer et al., “Benchmarking of Machine Learning for Anomaly based Intrusion Detection Systems in the CICIDS2017 Dataset,” IEEE access, vol. 9, pp. 22351-22370, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Abebe Diro et al., “Lightweight Authenticated-Encryption Scheme for Internet of Things Based on Publish-Subscribe Communication,” IEEE Access, vol. 8, pp. 60539-60551, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[9] P. Rajendra Prasad, 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]
[10] C. Edwin Singh, and S. Maria Celestin Vigila, “Fuzzy based Intrusion Detection System in Manet,” Measurement: Sensors, vol. 26, pp. 1-6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] 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]
[12] R. Reka et al., “Multi Head Self-Attention Gated Graph Convolutional Network based Multi Attack Intrusion Detection in MANET,” Computers & Security, vol. 136, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Saleh A. Alghamdi, “Novel Trust-Aware Intrusion Detection and Prevention System for 5G MANET-Cloud,” International Journal of Information Security, vol. 21, no. 3, pp. 469-488, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Uppalapati Srilakshmi et al., “An Improved Hybrid Secure Multipath Routing Protocol for MANET,” IEEE Access, vol. 9, pp. 163043-163053, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] M. Ganesh Karthik et al., “An Intrusion Detection Model Based on Hybridization of S-ROA in Deep Learning Model for MANET,” Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 48, no. 2, pp. 719-730, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Rahma Meddeb et al., “A Deep Learning-Based Intrusion Detection Approach for Mobile Ad-Hoc Network,” Soft Computing, vol. 27, no. 14, pp. 9425-9439, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Jiang Xie et al., “An Efficient Spectral Clustering Algorithm based on Granular-Ball,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 9, pp. 9743-9753, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Mohammad Dehghani et al., “Lyrebird Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems,” Biomimetics, vol. 8, no. 6, pp. 1-62, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Mandli Rami Reddy et al., “Energy-Efficient Cluster Head Selection in Wireless Sensor Networks using an Improved Grey Wolf Optimization Algorithm,” Computers, vol. 12, no. 2, pp. 1-17, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Shahnawaz Ahmad, Shabana Mehfuz, and Javed Beg, “An Efficient and Secure Key Management with the Extended Convolutional Neural Network for Intrusion Detection in Cloud Storage,” Concurrency and Computation: Practice and Experience, vol. 35, no. 23, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Vanlalruata Hnamte et al., “A Novel Two-Stage Deep Learning Model for Network Intrusion Detection: LSTM-AE,” IEEE Access, vol. 11, pp. 37131-37148, 2023.
[CrossRef] [Google Scholar] [Publisher Link]