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

Author(s)	Ms. Rubina S Pathan, S.A.Quadri
Country	India
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.
Keywords	Byzantine Fault Tolerance Distributed Networks Machine Learning Fault Node Detection Consensus Algorithms Support Vector Machine (SVM) Decision Tree Random Forest Performance Metrics Reliability in Distributed Systems
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 4, July-August 2025
Published On	2025-08-30
DOI	https://doi.org/10.36948/ijfmr.2025.v07i04.54117

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