Work

Comparative Study of Machine Learning Techniques for Byzantine Fault Node Detection in Distributed Networks

Year: 2025

Type: article

Abstract: Byzantine fault detection is a critical challenge in distributed networks such as blockchain, IoT, and cloud systems, where malicious nodes exhibit unpredictable behaviour that compromises network integrity. In this study, we present a comparative evaluation of three machine learning techniques such as Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM) for the task of Byzantine node detection.

Source: International Journal For Multidisciplinary Research

DOI: 10.36948/ijfmr.2025.v07i04.54117

Authors: Rubina Pathan, S.A.Quadri -

Institution: Visvesvaraya Technological University

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4413842810

FWCI:

Cites:

Cited by:

Related to: 10