Enhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack detection.

Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs. To optimize the model's efficiency, a unique DECEHGS algorithm combining Differential Evolution and Evolutionary Population Dynamics techniques is employed, enhancing both convergence and performance. The proposed model demonstrates significant improvements over existing methods, achieving an accuracy of 95%, a 12% increase in packet delivery ratio, and a 20% reduction in routing overhead compared to traditional techniques. These advancements underline the model's superiority in detecting malicious nodes, conserving energy, and ensuring reliable network performance. The comprehensive evaluation using MATLAB R2023a validates the proposed approach as an effective and energy-efficient solution for enhancing MANET security.