Blockchain Based Decentralized Replay Attack Detection for Large Scale Power Systems

TL;DR

This paper introduces a blockchain-based decentralized framework for detecting replay cyberattacks in large power systems, ensuring privacy and improving detection accuracy, timeliness, and scalability over traditional methods.

Contribution

It presents a novel blockchain-enabled Bayesian inference method for decentralized attack detection that outperforms existing gossip-based algorithms.

Findings

Achieves high detection accuracy in experiments

Outperforms gossip algorithms in timeliness and scalability

Maintains full privacy of sensor data

Abstract

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.