This paper investigates equilibrium strategies in networked control systems subject to false data injection (FDI) attacks, employing a game-theoretic approach. Our framework characterizes the dynamics that revolves around the freshness of the data from multiple sensors using the age of incorrect information (AoII) metric. The interaction between legitimate transmitters, aiming to minimize the system AoII and their transmission costs, and a malicious adversary, aiming to maximize AoII while managing FDI costs, is modeled as a non-cooperative game. We analytically demonstrate the existence and uniqueness of a Nash equilibrium (NE) and derive explicit conditions characterizing equilibrium resource allocation strategies. We also present examples of applications related to secure healthcare and automotive control. The numerical results validate our theoretical findings, highlighting the strategic impact of the system parameters, including drift rates, FDI and transmission costs, and resource constraints. Our analysis yields actionable guidelines for enhancing sensor security through parameter tuning and resource allocation.

We study a control system where sensor measurements are transmitted to a remote station. Information may become outdated due to system drift or compromised by malicious false data injection. To quantify the impact of staleness and inaccuracy in the information at the receiver’s side, we use Age of Incorrect Information (AoII). In particular, we consider the Excess AoII above a certain threshold as our key objective to minimize, which we argue to be a sensible goal for many real-time control systems. We adopt a game-theoretic framework to model the strategic interaction between a transmitter, which aims to minimize both Excess AoII and transmission costs, and a malicious agent, which seeks to maximize the same Excess AoII metric while minimizing its own costs. Our analysis reveals the existence of a Nash equilibrium for this game, and we investigate how the system parameters influence the adversary’s decision to attack, identifying the conditions under which an attack becomes advantageous or not.

We use Bayesian game theory to investigate the interaction between a system controller and an additional unknown agent in a cyber-physical system. The system controller performs some monitoring for real-time operation management, with the aim of minimizing the age of incorrect information (AoII). The additional agent reports some extra information, which ideally can serve to aid the controller and meet the same objective of decreasing AoII, but it is uncertain whether these actions are useful or correspond to (possibly international) false data injection in the system. The controller only has information in terms of probability of the legitimacy of this extra agent through a common prior, and also knows that, in case it is malicious, it will try to increase AoII instead. Our analysis reveals that, under rational behavior, an adversary can effectively masquerading as a sensor injecting legitimate data, as the controller can hardly distinguish the behavior of a true helper from that of an attacker. However, under variable data drift, the strategic behavior of the external agent can give away their type.

Critical Infrastructures (CIs) such as power grid, water and gas distribution are controlled by Industrial Control Systems (ICS). Sensors and actuators of a physical plant are managed by the ICS. Data and commands transmitted over the network from the Programmable Logic Controllers (PLCs) are saved and parsed within the Historian. Generally, this architecture guarantees to check for any process anomalies that may occur due to component failures and cyber attacks. The other use of this data allows activities such as forensic analysis. To secure the network is also crucial to protect the communication between devices. A cyber attack on the log devices could jeopardize any forensic analysis be it for maintenance, or discovering an attack trail. In this paper is proposed a strategy to secure plant operational data recorded in the Historian and data exchange in the network. An integrity checking mechanism, in combination with blockchain, is used to ensure data integrity. Data redundancy is achieved by applying an efficient replication mechanism and enables data recovery after an attack.

Over the last year, the Internet of Things (IoT) drove the development of cyber-physical systems, leading the convergence between information and operational technologies. This coupling improves performances and saves costs but increases the number of vulnerabilities and the attack surface to malicious actors. Consequently, it is mandatory to understand how IoT devices can be properly integrated into a more secure environment. This issue is even more crucial in the field of Critical Infrastructures, such as energy, water, transportation, and telecommunications. To this aim, this paper analyzes the concept of opacity for Discrete Event Systems (DES) and applies it to a real system. The opacity describes the ability of the system to keep some states secret even if an attacker knows the happening of some events. The proposed approach is validated by applying the concept of opacity on the analysis over a simple system. It is demonstrated that opacity can be guaranteed and exploited when systems having different levels of security are integrated.