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Intelligent and Converged Networks 2024, 5(1): 1-18 https://doi.org/10.23919/ICN.2024.0001
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Adaptive Marine Predator Optimization Algorithm (AOMA)–Deep Supervised Learning Classification (DSLC)based IDS framework for MANET security

Show Author's Information M. Sahaya Sheela1,  A. Gnana Soundari2,  Aditya Mudigonda3,  C. Kalpana4,  K. Suresh5,  K. Somasundaram6,  Yousef Farhaoui7( )
Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai 600069, India
Department of Computer Science and Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India
JNIAS School of Planning and Architecture, Hyderabad 500034, India
Department of Computer Science and Engineering, NPR College of Engineering and Technology, Natham Dindigul 624401, India
Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Poolangulathupatti 620009, India
Department of Computer Science and Engineering, Sri Muthukumaran Institute of Technology, Chennai 600069, India
Department of Computer Science, Moulay Ismail University, Meknes 5003, Morocco
Keywords:
Security, Intrusion Detection System (IDS), Mobile Ad-hoc Network (MANET), min-max normalization, Adaptive Marine Predator Optimization Algorithm (AOMA), Deep Supervise Learning Classification (DSLC)
Cite this article:
Sheela MS, Soundari AG, Mudigonda A, et al. Adaptive Marine Predator Optimization Algorithm (AOMA)–Deep Supervised Learning Classification (DSLC)based IDS framework for MANET security. Intelligent and Converged Networks, 2024, 5(1): 1-18. https://doi.org/10.23919/ICN.2024.0001
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Abstract Full text About this article

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 min-max 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.


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Adaptive Marine Predator Optimization Algorithm (AOMA)–Deep Supervised Learning Classification (DSLC)based IDS framework for MANET security

Show Author's information M. Sahaya Sheela1, A. Gnana Soundari2, Aditya Mudigonda3, C. Kalpana4, K. Suresh5, K. Somasundaram6, Yousef Farhaoui7( )
Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai 600069, India
Department of Computer Science and Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India
JNIAS School of Planning and Architecture, Hyderabad 500034, India
Department of Computer Science and Engineering, NPR College of Engineering and Technology, Natham Dindigul 624401, India
Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Poolangulathupatti 620009, India
Department of Computer Science and Engineering, Sri Muthukumaran Institute of Technology, Chennai 600069, India
Department of Computer Science, Moulay Ismail University, Meknes 5003, Morocco

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 min-max 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.

Keywords: Security, Intrusion Detection System (IDS), Mobile Ad-hoc Network (MANET), min-max normalization, Adaptive Marine Predator Optimization Algorithm (AOMA), Deep Supervise Learning Classification (DSLC)

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Received: 29 January 2023
Revised: 03 July 2023
Accepted: 20 August 2023
Published: 28 March 2024
Issue date: March 2024

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