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., 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:文章的目的是研究入侵攻击,然后开发一个新的网络攻击探测框架。 具体地说, 我们首先在实验室中设计和实施一个连锁网络。 这个连锁网络将有两个目的, 即为我们学习模型创造真正的交通数据( 包括正常数据和攻击数据), 并进行实时实验, 评估我们拟议入侵探测框架的性能。 根据我们的知识, 这是第一个在连锁网络网络网络的网络攻击实验室中合成的原始数据集。 然后我们提出一个新的合作学习模式, 以便有效地在连锁网络中部署, 侦测攻击。 这个拟议学习模式的主要理念是让连锁网络节点能够积极收集数据, 分享从数据中学到的知识, 然后与网络中的其他连锁节点交流知识。 这样, 我们不仅可以利用网络中所有节点的知识, 而且不需要收集所有原始数据, 在中央网络的网络中进行训练, 像常规的集中学习解决方案。 这个框架还可以避免暴露本地数据隐私的风险, 将隐私作为基于网络的过度测试的准确性测试, 从而明确显示我们97 的深度测试。