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

MANET Intrusion Detection Model Based on Dynamic Lyrebird Optimization with Hybrid Convolutional Neural Network and Long Short-Term Memory
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© 2025 by IJETT Journal
Volume-73 Issue-11
Year of Publication : 2025
Author : Divya Boya, Syed Shabbeer Ahmad
DOI : 10.14445/22315381/IJETT-V73I11P124

How to Cite?
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, 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.

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