[This corrects the article DOI: 10.1371/journal.pone.0168583.].

It is challenging to predict the precise location and severity of flood events, which can cause major disturbance to different public services, such as disruption to and the closure of critical transportation infrastructure. Therefore, approaches to enhance the resilience of these infrastructures are required. Serious games are new computing tools that have been applied in various fields with a combination of gameplay, learning and training. However, the application of serious games in intelligent transportation systems remains underutilized. As a result, the concept of a serious game is developed in this study to significantly improve transportation infrastructure resilience in an example of a flooding event. The serious game concept presented in this paper is being developed as part of the H2020-funded PRECINCT project (www.precinct.info).

[This corrects the article DOI: 10.1371/journal.pone.0168675.].

In this chapter, we adopt a holistic cross-layer viewpoint towards a hierarchical structure of ICS and the attack models. The physical layer is comprised of devices, controllers and the plant whereas the cyber layer consists of routers, protocols, and security agents and manager. The physical layer controllers are often designed to be robust, adaptive, and reliable for physical disturbances or faults. With the possibility of malicious behavior from the network, it is also essential for us to design physical layer defense that take into account the disturbances and delay resulting from routing and network traffic as well as the unexpected failure of network devices due to cyber-attacks. On the other hand, the cyber security policies are often designed without consideration of control performances. To ensure the continuous operability of the control system, it is equally important for us to design security policies that provide maximum level of security enhancement but minimum level of system overhead on the networked system. The physical and cyber aspects of control systems should be viewed holistically for analysis and design.

We study the significance of the common trusted relay assumption in quantum networks. While most practical implementations of quantum networks rely on trusted devices, the question of security without this assumption has been rarely addressed. Device independent security attempts to minimize the assumptions made on the quantum hardware, entanglement based methods try to avoid relays to the extent possible, and multipath transmission improves robustness and security by enforcing the attacker to conquer more than just a single intermediate node. Common to all these past studies is their focus on the physical layer and direct connections. We describe an attack from the networking and routing layer. Assuming at least one node that is not perfectly tamper-proof, meaning that an attacker has established a foothold to read traffic from the inside, we show how to exploit the eavesdropping detection mechanisms of the quantum key distribution (QKD) devices to cause traffic redirection over the vulnerable node, thus defeating security under the trusted node assumption. We experimentally demonstrate how the attack works on networks of different size and topology, and thereby further substantiate the significance of the trust assumptions for end-to-end security of QKD networks.

This work describes the PRAETORIAN system, an advanced framework for the protection of critical infrastructures from combined physical and cyber threats. It integrates components for detecting threats and generating alerts related with the physical and the cyber domains of Critical Infrastructures (CIs). These alerts are correlated by a hybrid situation awareness system that calculates the cascading effects of the attacks. Finally, a response coordination system manages notifications and assists decision making by providing mitigation actions and sharing information with the public and relevant authorities. The PRAETORIAN system was extensively validated by a large number of end users and was demonstrated in real-world environments in terms of providing support for the detection and mitigation of physical and/or cyber attacks in CIs.

In this article, we present a novel approach to learning finite automata with the help of recurrent neural networks. Our goal is not only to train a neural network that predicts the observable behavior of an automaton but also to learn its structure, including the set of states and transitions. In contrast to previous work, we constrain the training with a specific regularization term. We iteratively adapt the architecture to learn the minimal automaton, in the case where the number of states is unknown. We evaluate our approach with standard examples from the automata learning literature, but also include a case study of learning the finite-state models of real Bluetooth Low Energy protocol implementations. The results show that we can find an appropriate architecture to learn the correct minimal automata in all considered cases.

Given a set of points in the Euclidean space $\mathbb{R}^\ell$ with $\ell>1$, the pairwise distances between the points are determined by their spatial location and the metric $d$ that we endow $\mathbb{R}^\ell$ with. Hence, the distance $d(\mathbf x,\mathbf y)=\delta$ between two points is fixed by the choice of $\mathbf x$ and $\mathbf y$ and $d$. We study the related problem of fixing the value $\delta$, and the points $\mathbf x,\mathbf y$, and ask if there is a topological metric $d$ that computes the desired distance $\delta$. We demonstrate this problem to be solvable by constructing a metric to simultaneously give desired pairwise distances between up to $O(\sqrt\ell)$ many points in $\mathbb{R}^\ell$. We then introduce the notion of an $\varepsilon$-semimetric $\tilde{d}$ to formulate our main result: for all $\varepsilon>0$, for all $m\geq 1$, for any choice of $m$ points $\mathbf y_1,\ldots,\mathbf y_m\in\mathbb{R}^\ell$, and all chosen sets of values $\{\delta_{ij}\geq 0: 1\leq i<j\leq m\}$, there exists an $\varepsilon$-semimetric $\tilde{\delta}:\mathbb{R}^\ell\times \mathbb{R}^\ell\to\mathbb{R}$ such that $\tilde{d}(\mathbf y_i,\mathbf y_j)=\delta_{ij}$, i.e., the desired distances are accomplished, irrespectively of the topology that the Euclidean or other norms would induce. We showcase our results by using them to attack unsupervised learning algorithms, specifically $k$-Means and density-based (DBSCAN) clustering algorithms. These have manifold applications in artificial intelligence, and letting them run with externally provided distance measures constructed in the way as shown here, can make clustering algorithms produce results that are pre-determined and hence malleable. This demonstrates that the results of clustering algorithms may not generally be trustworthy, unless there is a standardized and fixed prescription to use a specific distance function.

Critical Infrastructures (CIs), which serve as the foundation of our modern society, are facing increasing risks from cyber threats, physical attacks, and natural disasters. Additionally, the interdependencies between CIs throughout their operational lifespan can also significantly impact their integrity and safety. As a result, enhancing the resilience of CIs has emerged as a top priority for many countries, including the European Union. This involves not only understanding the threats/attacks themselves but also gaining knowledge about the areas and infrastructures that could potentially be affected. A European Union-funded project named PRECINCT (Preparedness and Resilience Enforcement for Critical INfrastructure Cascading Cyber-Physical Threats), under the Horizon 2020 program, tries to connect private and public stakeholders of CIs in a specific geographical area. The key objective of this project is to establish a common cyber-physical security management approach that will ensure the protection of both citizens and infrastructures, creating a secure territory. This paper presents the components of PRECINCT, including a directory of PRECINCT Critical Infrastructure Protection (CIP) blueprints. These blueprints support CI communities in designing integrated ecosystems, operating and replicating PRECINCT components (or toolkits). The integration enables coordinated security and resilience management, incorporating improved 'installation-specific' security solutions. Additionally, Serious Games (SG), and Digital Twins (DT) are a significant part of this project, serving as a novel vulnerability evaluation method for analysing complicated multi-system cascading effects in the PRECINCT Living Labs (LLs). The use of SG supports the concentrated advancement of innovative resilience enhancement services.

Increasing interdependencies between critical infrastructures and digitization increase the vulnerability to cyber-attacks and cyber-physical attacks. Incidents have multiple direct and indirect consequences, including cascading effects, and a formal analysis is strongly recommended to understand these effects. This paper shows how threat identification and impact evaluation for interdependent critical infrastructures can be supported by two existing tools. The approach is illustrated with an example based on a running EU project.