Blockchain-Based Decentralized Replay Attack Detection for Large-Scale Power Systems
针对大规模电力系统中传感器数据集中分析困难的问题，提出一种基于区块链的去中心化框架，利用贝叶斯推理检测重放攻击，保护数据隐私，并在私有以太坊上验证了其检测质量和可扩展性优于传统广播方法。
Large-scale power systems are composed 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 article, 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.