COMPREHENSIVE STUDY ON MALICIOUS NODE DETECTION MODELS IN MOBILE NETWORKS

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GOTTE RANJITH KUMAR; Dr.K.Suresh babu

COMPREHENSIVE STUDY ON MALICIOUS NODE DETECTION MODELS IN MOBILE NETWORKS

Authors: GOTTE RANJITH KUMAR, Dr.K.Suresh babu

Unique Paper ID: 179071
Volume: 11
Issue: 12
PageNo: 5480-5486

Keywords: Mobile networks Malicious node detection Handover Authentication process Secure Blockchain-Based Authentication and Key Agreement scheme and the Elliptic Curve Cryptography-Based Diffie-Hellman method.

Abstract:
Mobile networks enable communication through radio waves among users, over large geographical regions. However, the increasing number of interconnected devices in these networks heightens the potential for malicious attacks. These networks are highly vulnerable to malicious nodes. Robust security protocols, anomaly detection mechanisms, frequent software updates, encryption, and user awareness are essential strategies to protect mobile networks. Furthermore, machine learning (ML) and deep learning (DL) techniques offer significant improvements in anomaly detection capabilities. Despite these advances, mobile networks face ongoing challenges, such as resource constraints, high false positive rates, and sophisticated malicious nodes that mimic legitimate users. This paper explores various malicious node detection mechanisms, highlighting their limitations, and proposes that deep learning-based methods offer improved efficiency in terms of resource and energy consumption. A comparative analysis is conducted using three detection mechanisms: the Anonymous Handover Authentication (AHA) process, a Secure Blockchain-Based Authentication and Key Agreement (5GSBA) scheme, and the Elliptic Curve Cryptography-Based Diffie-Hellman (ECDH) method.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 5480-5486

COMPREHENSIVE STUDY ON MALICIOUS NODE DETECTION MODELS IN MOBILE NETWORKS

Available:https://ijirt.org/article?manuscript=179071

05 Dec, 2024

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Journal no 47859

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