Exploring Adversarial Threat Models in Cyber Physical Battery Systems

TL;DR

This paper investigates adversarial threats in cyber physical battery systems, proposing a control-based attack model and analyzing its implications to enhance system resilience against cyber attacks.

Contribution

It introduces a systematic adversarial threat model for cyber physical batteries using an optimal control framework, supported by theoretical analysis and experimental validation.

Findings

Identified potential attack strategies on battery systems.

Developed a control-based adversarial attack generation framework.

Validated the attack model with experimental data.

Abstract

Technological advancements like the Internet of Things (IoT) have facilitated data exchange across various platforms. This data exchange across various platforms has transformed the traditional battery system into a cyber physical system. Such connectivity makes modern cyber physical battery systems vulnerable to cyber threats where a cyber attacker can manipulate sensing and actuation signals to bring the battery system into an unsafe operating condition. Hence, it is essential to build resilience in modern cyber physical battery systems (CPBS) under cyber attacks. The first step of building such resilience is to analyze potential adversarial behavior, that is, how the adversaries can inject attacks into the battery systems. However, it has been found that in this under-explored area of battery cyber physical security, such an adversarial threat model has not been studied in a systematic manner. In this study, we address this gap and explore adversarial attack generation policies based on optimal control framework. The framework is developed by performing theoretical analysis, which is subsequently supported by evaluation with experimental data generated from a commercial battery cell.