Differential Privacy for Regulatory Compliance in Cyberattack Detection on Critical Infrastructure Systems

TL;DR

This paper introduces a differentially private cyberattack detection framework for critical infrastructure systems that balances regulatory compliance, stakeholder privacy, and detection accuracy using a two-phase privacy scheme.

Contribution

It proposes a novel two-phase privacy scheme for cyberattack detection that protects sensor data privacy while maintaining high detection accuracy in critical infrastructure networks.

Findings

Privacy guarantees comparable to non-DP methods

Effective detection across various attack scenarios

Robust privacy protection of covariance and test statistics

Abstract

Industrial control systems are a fundamental component of critical infrastructure networks (CIN) such as gas, water and power. With the growing risk of cyberattacks, regulatory compliance requirements are also increasing for large scale critical infrastructure systems comprising multiple utility stakeholders. The primary goal of regulators is to ensure overall system stability with recourse to trustworthy stakeholder attack detection. However, adhering to compliance requirements requires stakeholders to also disclose sensor and control data to regulators raising privacy concerns. In this paper, we present a cyberattack detection framework that utilizes differentially private (DP) hypothesis tests geared towards enhancing regulatory confidence while alleviating privacy concerns of CIN stakeholders. The hallmark of our approach is a two phase privacy scheme that protects the privacy of covariance, as well as the associated sensor driven test statistics computed as a means to generate alarms. Theoretically, we show that our method induces a misclassification error rate comparable to the non-DP cases while delivering robust privacy guarantees. With the help of real-world datasets, we show the reliability of our DP-detection outcomes for a wide variety of attack scenarios for interdependent stakeholders.