Invisible Manipulation Deep Reinforcement Learning Enhanced Stealthy Attacks on Battery Energy Management Systems

TL;DR

This paper presents a novel stealthy cyber-attack method on battery energy management systems using deep reinforcement learning to generate undetectable false data, guiding the system to attacker-desired states without detection.

Contribution

It introduces 'invisible manipulation,' a new attack approach leveraging DRL to craft undetectable false data in BEMS, enabling covert control of system states.

Findings

Effective evasion of bad data detection algorithms.

Successful manipulation of BEMS to reach targeted operational states.

Validated on a high-fidelity microgrid simulation testbed.

Abstract

This paper introduces "invisible manipulation," an innovative cyber-attack mechanism achieved through strategically timed stealthy false data injection attacks (SFDIAs). By stealthily manipulating measurements of a critical asset prior to the target time period, the attacker can subtly guide the engineering system toward a predetermined operational state without detection. Using the battery energy management system (BEMS) as a case study, we employ deep reinforcement learning (DRL) to generate synthetic measurements, such as battery voltage and current, that align closely with actual measurements. These synthetic measurements, falling within the acceptable error margin of residual-based bad data detection algorithm provided by state estimation, can evade detection and mislead Extended Kalman-filter-based State of Charge estimation. Subsequently, considering the deceptive data as valid inputs, the BEMS will operate the BESS towards the attacker desired operational states when the targeted time period come. The use of the DRL-based scheme allows us to covert an online optimization problem into an offline training process, thereby alleviating the computational burden for real-time implementation. Comprehensive testing on a high-fidelity microgrid real-time simulation testbed validates the effectiveness and adaptability of the proposed methods in achieving different attack objectives.