Ethereum Transaction Anomaly Detection by Integrating Machine Learning Models and Fuzzy Networks for Enhanced Security and Real-Time Monitoring

Abstract

The objective of this research is to develop an R&D (Research and Development) for the hardiness relay alert system, including applying the machine learning, and the fuzzy logic networks for the real time Ethereum transaction 'match failure' detection and the improved Ethereum blockchain security.As an example, the system is computing on the transactions due to the fact the system for transaction analysis corresponds with concrete intrinsic characteristics and thus it mainly takes out suspicious or malicious transactions.The logistic regression, support vector machines (SVM) decision tree and random forests are used in this research and optimized by grid search.Finally, on the other hand, uncertainty problems and false alarms are solved where fuzzy membership functions are used to put transaction attributes into linguistic hobbled variables (such as 'low', 'medium' and 'high').The conclusion of this descriptive research is that fuzzy logic integration with machine learning can improve the approach of anomaly mediation compared to the rules based approach and it is superior to rules based approach.Finally, the effectiveness of the models is detailed and replicated in various graphical representations of the decision making process and membership functions to show that the system can be deployed in real time to secure blockchain networks.