Active techniques have been introduced to give better detectability performance for cyber-attack diagnosis in cyber–physical systems (CPS). In this paper, switching multiplicative watermarking is considered, whereby we propose an optimal design strategy to define switching filter parameters. Optimality is evaluated exploiting the so-called output-to-output gain of the closed-loop system, including some supposed attack dynamics. A worst-case scenario of a matched covert attack is assumed, presuming that an attacker with full knowledge of the closed-loop system injects a stealthy attack of bounded energy. Our algorithm, given watermark filter parameters at some time instant, provides optimal next-step parameters. Analysis of the algorithm is given, demonstrating its features, and demonstrating that through initialization of certain parameters outside of the algorithm, the parameters of the multiplicative watermarking can be randomized. Simulation shows how, by adopting our method for parameter design, the attacker’s impact on performance diminishes.

In this introductory chapter, we illustrate the book’s motivation and objective. In particular, the book takes its raison d’être from the need for protecting Cyber-Physical Systems (CPSs) against threats originating either in the cyber or in the physical domain. Exploring the concepts of safety, security, and privacy for CPSs thus emerged as the natural goal to reach. In order to better support this objective and to help the reader to navigate the book contents, a taxonomy of the above-mentioned concepts is introduced, based on a set of three triads, including the well-known Confidentiality, Integrity, and Availability triad which was introduced in the Information Technology security literature.

This chapter addresses the problem of detecting stealthy data injection attacks on sensor measurements in a networked control system. A multiplicative watermarking scheme is proposed, where the data from each sensor is post-processed by a time-varying filter called watermark generator. At the controller’s side, the watermark is removed from each channel by another filter, called the watermark remover, thus reconstructing the original signal. The parameters of each remover are matched to those of the corresponding generator, and are supposed to be a shared secret not known by the attacker. The rationale for time-varying watermarks is to allow model-based schemes to detect otherwise stealthy attacks by constantly introducing mismatches between the actual and the nominal dynamics used by the detector. A specific model-based diagnosis algorithm is designed to this end. Under the proposed watermarking scheme, the robustness and the detectability properties of the model-based detector are analyzed and guidelines for designing the watermarking filters are derived. Distinctive features of the proposed approach, with respect to other solutions like end-to-end encryption, are that the scheme is lightweight enough to be applied also to legacy control systems, the absence of side-effects such as delays, and the possibility of utilizing a robust controller to operate the closed-loop system in the event of the transmitter and receiver losing synchronization of their watermarking filters. The results are illustrated through numerical examples.

Water management infrastructures such as floodgates are critical and increasingly operated by Industrial Control Systems (ICS). These systems are becoming more connected to the internet, either directly or through the corporate networks. This makes them vulnerable to cyber-attacks. Abnormal behaviour in floodgates operated by ICS could be caused by both (intentional) attacks and (accidental) technical failures. When operators notice abnormal behaviour, they should be able to distinguish between those two causes to take appropriate measures, because for example replacing a sensor in case of intentional incorrect sensor measurements would be ineffective and would not block corresponding the attack vector. In the previous work, we developed the attack-failure distinguisher framework for constructing Bayesian Network (BN) models to enable operators to distinguish between those two causes, including the knowledge elicitation method to construct the directed acyclic graph and conditional probability tables of BN models. As a full case study of the attack-failure distinguisher framework, this paper presents a BN model constructed to distinguish between attacks and technical failures for the problem of incorrect sensor measurements in floodgates, addressing the problem of floodgate operators. We utilised experts who associate themselves with the safety and/or security community to construct the BN model and validate the qualitative part of constructed BN model. The constructed BN model is usable in water management infrastructures to distinguish between intentional attacks and accidental technical failures in case of incorrect sensor measurements. This could help to decide on appropriate response strategies and avoid further complications in case of incorrect sensor measurements.

In this final chapter, we summarize the results and conclusions from each individual contribution in the book, and take the opportunity to provide our and other authors’ insights into possible future developments in the fields of safety, security, and privacy for cyber-physical systems. In doing so, we make use of the taxonomy presented in Chap.  1, and use its pictorial representation to both quantify the breadth of current results and to identify unexplored and promising research avenues.