  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.
