  With the escalating prevalence of malicious activities exploiting
vulnerabilities in blockchain systems, there is an urgent requirement for
robust attack detection mechanisms. To address this challenge, this paper
presents a novel collaborative learning framework designed to detect attacks in
blockchain transactions and smart contracts by analyzing transaction features.
Our framework exhibits the capability to classify various types of blockchain
attacks, including intricate attacks at the machine code level (e.g., injecting
malicious codes to withdraw coins from users unlawfully), which typically
necessitate significant time and security expertise to detect. To achieve that,
the proposed framework incorporates a unique tool that transforms transaction
features into visual representations, facilitating efficient analysis and
classification of low-level machine codes. Furthermore, we propose a customized
collaborative learning model to enable real-time detection of diverse attack
types at distributed mining nodes. In order to create a comprehensive dataset,
we deploy a pilot system based on a private Ethereum network and conduct
multiple attack scenarios. 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\% through
extensive simulations and real-time experiments 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.
