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

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.