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

DOI

10.2991/978-94-6463-704-5_8How to use a DOI?

Keywords

Ethereum; Anomaly Detection; Machine Learning; Fuzzy Logic; Blockchain Security; Real-Time Monitoring; Smart Contracts; Logistic Regression; SVM; Decision Trees; Random Forests

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.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - K. Rajesh
AU  - K. Venkatesh
PY  - 2025
DA  - 2025/04/30
TI  - Ethereum Transaction Anomaly Detection by Integrating Machine Learning Models and Fuzzy Networks for Enhanced Security and Real-Time Monitoring
BT  - Proceedings of the International Conference on Smart Health and Intelligent Technologies (ICSHit-2024)
PB  - Atlantis Press
SP  - 76
EP  - 91
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-704-5_8
DO  - 10.2991/978-94-6463-704-5_8
ID  - Rajesh2025
ER  -