Large scale power systems are comprised of regional utilities with assets that stream sensor readings in real time. In order to detect cyberattacks, the globally acquired, real time sensor data needs to be analyzed in a centralized fashion. However, owing to operational constraints, such a centralized sharing mechanism turns out to be a major obstacle. In this paper, we propose a blockchain based decentralized framework for detecting coordinated replay attacks with full privacy of sensor data. We develop a Bayesian inference mechanism employing locally reported attack probabilities that is tailor made for a blockchain framework. We compare our framework to a traditional decentralized algorithm based on the broadcast gossip framework both theoretically as well as empirically. With the help of experiments on a private Ethereum blockchain, we show that our approach achieves good detection quality and significantly outperforms gossip driven approaches in terms of accuracy, timeliness and scalability.
翻译:大型电力系统由区域公用设施组成,拥有实时传感器读数的资产。为了检测网络攻击,需要集中分析全球获取的实时感应数据。然而,由于操作上的制约因素,这种中央共享机制被证明是一个重大障碍。在本文中,我们建议建立一个基于街区的分散化框架,用传感器数据的完全隐私来检测协调重播攻击。我们开发了一种巴伊西亚推论机制,使用当地报告的攻击概率,这是为块链框架量身定制的。我们将我们的框架与基于广播八卦框架的传统的分散算法相比较,既有理论的,也有经验的。在对私人Etereum块链的实验的帮助下,我们展示了我们的方法在准确性、及时性和可缩放性方面都取得了良好的探测质量,大大超越了八卦所推动的方法。