论文标题
基于区块链的大规模电力系统的分散重播攻击检测
Blockchain Based Decentralized Replay Attack Detection for Large Scale Power Systems
论文作者
论文摘要
大规模电源系统由区域公用事业组成，其资产可以实时流式传感器读取。为了检测网络攻击，需要以集中式的方式对全球获得的实时传感器数据进行分析。但是，由于运营限制，这种集中的共享机制被证明是一个主要障碍。在本文中，我们提出了一个基于区块链的去中心化框架，用于使用传感器数据的全部隐私来检测协调的重播攻击。我们开发了一种使用本地报道的攻击概率的贝叶斯推论机制，该攻击概率是为区块链框架量身定制的。我们将框架与基于广播八卦框架的传统分散算法进行比较。在私人以太坊区块链上的实验的帮助下，我们表明我们的方法可实现良好的检测质量，并且在准确性，及时性和可扩展性方面显着优于八卦驱动的方法。
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.
