Collaborative Learning Framework to Detect Attacks in Transactions and Smart Contracts

This paper presents a novel collaborative learning framework designed to detect attacks in blockchain transactions and smart contracts by analyzing transaction features. The proposed framework incorporates a unique tool that transforms transaction features into visual representations, facilitating efficient analysis and classification of low-level machine code for attack detection. Furthermore, we propose an advanced collaborative learning model to enable real-time detection of diverse attack types at distributed mining nodes. In order to evaluate the performance of our proposed framework, we deploy a pilot system based on a private Ethereum network and conduct multiple attack scenarios to generate a novel dataset. To the best of our knowledge, our dataset is the most comprehensive and diverse collection of transactions and smart contracts synthesized in a laboratory for cyberattack detection in blockchain systems. Our framework achieves a detection accuracy of approximately 94% in extensive simulations, which is about 22% higher than that of a centralized learning model. In real-time experiments, it achieves 91% accuracy with a throughput of over 2,150 transactions per second. These compelling results validate the efficacy of our framework and showcase its adaptability in addressing real-world cyberattack scenarios.