Exposing Cyber-Physical System Weaknesses by Implicitly Learning their Underlying Models

Cyber-Physical Systems (CPS) plays a critical role in today's social life, especially with occasional pandemic events. With more reliance on the cyber operation of infrastructures, it is important to understand attacking mechanisms in CPS for potential solutions and defenses, where False Data Injection Attack (FDIA) is an important class. FDIA methods in the literature require the mathematical CPS model and state variable values to create an efficient attack vector, unrealistic for many attackers in the real world. Also, they do not have performance guarantee. This paper shows that it is possible to deploy a FDIA without having the CPS model and state variables information. Additionally, we prove a theoretic bound for the proposed method. Specifically, we design a scheme that learns an implicit CPS model to create tampered sensor measurements to deploy an attack based only on historical data. The proposed framework utilizes a Wasserstein generative adversarial network with two regularization terms to create such tampered measurements also known as adversarial examples. To build an attack with confidence, we present a proof based on convergence in distribution and Lipschitz norm to show that our method captures the real observed measurement distribution. This means that our model learns the complex underlying processes from the CPSs. We demonstrate the robustness and universality of our proposed framework based on two diversified adversarial examples with different systems, domains, and datasets.