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MLA: INTELLECTUAL DUAL KEY-BASED NODE AUTHENTICATION WITH MASTER LINKED AUDITOR NODE BEHAVIOUR-BASED MALICIOUS NODE DETECTION FOR SECURE DATA TRANSMISSION IN 6G ENABLED WSN

By
Pavan Vamsi Mohan Movva Orcid logo ,
Pavan Vamsi Mohan Movva
Contact Pavan Vamsi Mohan Movva

Department of computer engineering, Koneru Lakshmaiah Foundation, Vaddeswaram , Guntur , India

Radhika Rani Chintala Orcid logo
Radhika Rani Chintala

Department of computer engineering, Koneru Lakshmaiah Foundation, Vaddeswaram , Guntur , India

Abstract

This study focuses on the ever-increasing challenge of ensuring the integrity of Sixth Generation (6G) enabled Wireless Sensor Networks (WSNs), which are highly vulnerable to malicious node attacks, thereby compromising network data integrity and efficiency. Conventional methods of cryptography do not necessarily resist advanced attacks like selective forwarding, where network nodes (malicious nodes) interfere with the network by dropping, delaying, or modifying data packets. To counteract such risks, the paper proposes a new model, the Intellectual Dual Key-based Node Authentication with Master Linked Auditor (IDKNA-MLA-MND). It is a framework that combines dual-key authentication and behavior-based auditing using a Master Node and an Auditor Node to audit the entire network. The most important innovation of such an approach is the division of responsibilities between the Master Node, which takes decisions based on behavior data, and the Auditor Node, which continuously monitors node behavior indicative of a malicious action. By verifying the node's identity and eliminating impersonation, the dual-key system ensures secure data transmission. The cross-layer approach in the methodology integrates cryptographic security and behavioral auditing, making it more resilient to both insider threats and adaptive attacks. The proposed model's performance is tested through a large number of simulations, which show it to be more efficient than the current models. In particular, the IDKNA-MLA-MND framework achieved 98.5% detection accuracy for malicious nodes and required much less time to detect both malicious and benign nodes. Moreover, it was demonstrated that the model has low communication overhead and energy consumption, making it very efficient for large-scale WSN implementation. The results indicate that the model is a promising way of improving the security and reliability of WSNs in practice.

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This is an open access article distributed under the  Creative Commons Attribution Non-Commercial License (CC BY-NC) License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 

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