Adaptive Marine Predator Optimization Algorithm (AOMA)-Deep Supervised Learning Classification (DSLC) Based IDS Framework for MANET Security

Abstract

Due to the dynamic nature and node mobility, assuring the security of Mobile Ad-hoc Networks (MANET) is one of the difficult and challenging tasks today. In MANET, the Intrusion Detection System (IDS) is crucial because it aids in the identification and detection of malicious attacks that impair the network's regular operation. Different machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of MANET. However, it still has significant flaws, including increased algorithmic complexity, lower system performance, and a higher rate of misclassification. Therefore, the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning models. Here, the minmax normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields, which increases the overall intrusion detection performance of classifier. Then, a novel Adaptive Marine Predator Optimization Algorithm (AOMA) is implemented to choose the optimal features for improving the speed and intrusion detection performance of classifier. Moreover, the Deep Supervise Learning Classification (DSLC) mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training operations. During evaluation, the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.