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Optimizing Manet Performance with Improvised Algorithmic Innovations for Enhanced Connectivity and Security


Affiliations
1 Department of Computer Science and Engineering, Government Engineering College, Jehanabad, India
2 Department of Physics, Raghuveer Singh Government Degree College, India
3 Department of Electronics and Telecommunication Engineering, Siddhant College of Engineering, India
4 Department of Information Technology, Dr. J.J. Magdum College of Engineering, India
5 Department of Computer Applications, Manipal University Jaipur, India
6 Department of Artificial Intelligence and Data Science, Dr. J.J. Magdum College of Engineering, India

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Mobile Ad-hoc Networks (MANETs) face significant challenges in maintaining connectivity and security due to their dynamic and decentralized nature. Passive attacks, such as eavesdropping and traffic analysis, pose a critical threat to MANET. This study proposes a novel algorithm, Radial ResNet, tailored for the classification of passive attacks in MANETs. The algorithm integrates radial basis function networks with residual networks (ResNet) to enhance classification accuracy and efficiency. Experimental results demonstrate the effectiveness of Radial ResNet, achieving an average classification accuracy of 95.2% across various passive attack scenarios. Moreover, the algorithm exhibits improved computational efficiency compared to traditional methods, reducing processing overhead by 30%. The proposed approach not only enhances security by accurately identifying passive attacks but also optimizes network performance by mitigating resource-intensive computations.

Keywords

MANETs, Passive Attacks, Radial ResNet, Classification, Security
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  • Optimizing Manet Performance with Improvised Algorithmic Innovations for Enhanced Connectivity and Security

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Authors

Raja Ram Sah
Department of Computer Science and Engineering, Government Engineering College, Jehanabad, India
Devendra Kumar Sahu
Department of Physics, Raghuveer Singh Government Degree College, India
Nanda Satish Kulkarni
Department of Electronics and Telecommunication Engineering, Siddhant College of Engineering, India
K. Venkata Ramana
Department of Information Technology, Dr. J.J. Magdum College of Engineering, India
Shikha Maheshwari
Department of Computer Applications, Manipal University Jaipur, India
E. Nagarjuna
Department of Artificial Intelligence and Data Science, Dr. J.J. Magdum College of Engineering, India

Abstract


Mobile Ad-hoc Networks (MANETs) face significant challenges in maintaining connectivity and security due to their dynamic and decentralized nature. Passive attacks, such as eavesdropping and traffic analysis, pose a critical threat to MANET. This study proposes a novel algorithm, Radial ResNet, tailored for the classification of passive attacks in MANETs. The algorithm integrates radial basis function networks with residual networks (ResNet) to enhance classification accuracy and efficiency. Experimental results demonstrate the effectiveness of Radial ResNet, achieving an average classification accuracy of 95.2% across various passive attack scenarios. Moreover, the algorithm exhibits improved computational efficiency compared to traditional methods, reducing processing overhead by 30%. The proposed approach not only enhances security by accurately identifying passive attacks but also optimizes network performance by mitigating resource-intensive computations.

Keywords


MANETs, Passive Attacks, Radial ResNet, Classification, Security