International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Securing Smart Contracts in Fog Computing: Machine Learning-Based Attack Detection for Registration and Resource Access Granting

A.GowthamAssistant Professor, Department of Computer Science and Engineering and Cyber Security (UG), Madanapalle Institute of Technology & Science (Autonomous), Madanapalle, IndiaK.Nanda Kishore ReddyUG Scholar, Department of Computer Science and Engineering and Cyber Security (UG), Madanapalle Institute of Technology & Science (Autonomous), Madanapalle, IndiaM.Somu Sekhar NaikUG Scholar, Department of Computer Science and Engineering and Cyber Security (UG), Madanapalle Institute of Technology & Science (Autonomous), Madanapalle, India

Vol 9 No 2025 (2025): Volume 9, Special Issue of ICCIS-2025 May 2025 | Pages: 48-54

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 11-06-2025

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Abstract

Attack resistance in smart contracts has become one of the paramount concerns in this fog computing evolving landscape. Based on this problem, this study utilizes machine learning for the detection of attacks from the analysis of Ethereum transaction data and smart contract interactions. There are different methods for feature extraction: Term Frequency-Inverse Document Frequency (TF-IDF), Bag of Words (BoW), and N-gram methods for converting raw data into readable formats by machine. Several machine learning classifiers, including XGBoost, Random Forest, Light Gradient Boosting, and Extra Trees, are applied to detect the attacks. These models should offer high detection accuracy with low computational complexity, enabling scalability for real-time fog computing applications. Experimental results show that Extra Trees, with N-gram features, outperformed other models by achieving an accuracy of 83%. The proposed system is promising toward the efficient detection and classification of attacks in a dynamic fog environment.

Keywords

Fog Computing, Smart Contracts, Machine Learning, Attack Detection, Ethereum, Feature Extraction, Computational Efficiency


Citation of this Article

A.Gowtham, K.Nanda Kishore Reddy, & M.Somu Sekhar Naik. (2025). Securing Smart Contracts in Fog Computing: Machine Learning-Based Attack Detection for Registration and Resource Access Granting. In proceeding of Second International Conference on Computing and Intelligent Systems (ICCIS-2025), published in IRJIET, Volume 9, Special Issue ICCIS-2025, pp 48-54. Article DOI https://doi.org/10.47001/IRJIET/2025.ICCIS-202507

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
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