This article aims to study intrusion attacks and then develop a novel cyberattack detection framework for blockchain networks. Specifically, we first design and implement a blockchain network in our laboratory. This blockchain network will serve two purposes, i.e., to generate the real traffic data (including both normal data and attack data) for our learning models and implement real-time experiments to evaluate the performance of our proposed intrusion detection framework. To the best of our knowledge, this is the first dataset that is synthesized in a laboratory for cyberattacks in a blockchain network. We then propose a novel collaborative learning model that allows efficient deployment in the blockchain network to detect attacks. The main idea of the proposed learning model is to enable blockchain nodes to actively collect data, share the knowledge learned from its data, and then exchange the knowledge with other blockchain nodes in the network. In this way, we can not only leverage the knowledge from all the nodes in the network but also do not need to gather all raw data for training at a centralized node like conventional centralized learning solutions. Such a framework can also avoid the risk of exposing local data's privacy as well as the excessive network overhead/congestion. Both intensive simulations and real-time experiments clearly show that our proposed collaborative learning-based intrusion detection framework can achieve an accuracy of up to 97.7% in detecting attacks.

翻译：本文旨在研究入侵攻击，并针对区块链网络提出一种新颖的网络攻击检测框架。具体而言，我们首先在实验室中设计并实现了一个区块链网络。该区块链网络将服务于两个目的：其一为学习模型生成真实流量数据（包括正常数据和攻击数据），其二为实施实时实验以评估所提入侵检测框架的性能。据我们所知，这是首个在实验室中为区块链网络合成的网络攻击数据集。随后，我们提出一种新颖的协作学习模型，该模型能够高效部署于区块链网络中用于检测攻击。所提学习模型的核心思想是使区块链节点能够主动收集数据，共享从其数据中学到的知识，并与网络中其他区块链节点交换知识。通过这种方式，我们不仅能够利用网络中所有节点的知识，而且无需像传统集中式学习方法那样将所有原始数据集中到一个中心节点进行训练。该框架还能避免暴露本地数据隐私的风险以及过度网络开销/拥塞问题。大量仿真和实时实验均表明，我们提出的基于协作学习的入侵检测框架在检测攻击方面准确率可达97.7%。