Reinforcement Learning for Supply Chain Attacks Against Frequency and Voltage Control

TL;DR

This paper explores how reinforcement learning can be used to develop sophisticated supply chain attacks on power system frequency and voltage control devices, highlighting potential vulnerabilities and defense implications.

Contribution

It introduces a novel application of reinforcement learning to simulate intelligent supply chain attacks on power grid control systems.

Findings

Reinforcement learning can generate adaptive attack strategies.

Simulated attacks impact frequency and voltage regulation.

Method offers insights for improving power system defenses.

Abstract

The ongoing modernization of the power system, involving new equipment installations and upgrades, exposes the power system to the introduction of malware into its operation through supply chain attacks. Supply chain attacks present a significant threat to power systems, allowing cybercriminals to bypass network defenses and execute deliberate attacks at the physical layer. Given the exponential advancements in machine intelligence, cybercriminals will leverage this technology to create sophisticated and adaptable attacks that can be incorporated into supply chain attacks. We demonstrate the use of reinforcement learning for developing intelligent attacks incorporated into supply chain attacks against generation control devices. We simulate potential disturbances impacting frequency and voltage regulation. The presented method can provide valuable guidance for defending against supply chain attacks.