Machine Learning-Based Improvement of Smart Contract Security in Fog Computing Using Word2vec And Bert

Abstract

Fog computing extends cloud computing services closer to users, improving efficiency and reducing latency. Smart contracts play a key role in authentication and resource access management within this framework. As the adoption of smart contracts in fog computing grows, ensuring their security has become a major challenge. This study enhances smart contract attack detection in fog computing using machine learning techniques. A dataset of 818 smart contracts was collected from “etherscan.io.” Feature extraction was performed using Word2Vec and BERT, while feature selection was done using the information gain method. The Random Forest (RF) and Extra Trees Classifier (ETC) achieved the highest accuracy of 0.91 with Word2Vec, while the LightGBM (LGBM) classifier attained 0.90 accuracy using BERT. These results demonstrate the effectiveness of machine learning models in improving smart contract security within fog computing environments.