As Industrial Cyber-Physical Systems (ICPS) become increasingly interconnected, they are exposed to a growing array of hybrid threats that target both digital and physical assets. A central challenge lies in identifying and implementing effective security countermeasures that mitigate such threats without disrupting critical operations. In this context, Decision Support Systems (DSS) offer a promising avenue for enhancing incident response. This can be done by assisting security operators in selecting optimal mitigation strategies. However, existing DSS solutions often face significant limitations, including poor scalability, high computational demands, and dependence on simulated attack scenarios that may not reflect real-world conditions. To overcome these challenges, we propose a novel hybrid knowledge-based DSS for intrusion response in ICPSs. Our approach integrates expert-driven evaluations with dynamic risk assessment to ensure both scalability and computational efficiency. Risk is continuously assessed using multiple Bayesian Networks. Moreover, expert knowledge is incorporated through an adapted Analytic Hierarchy Process (AHP) framework, capable of handling incomplete information, to evaluate the effectiveness and operational impact of available countermeasures. A cyber-physical detection module further enhances the system by providing detected events along with their associated detection probabilities. The optimal response strategy is selected by solving a bi-objective optimization problem. In particular, the problem amounts to minimizing both residual risk and the potential negative impact on ICPS functionality. The proposed DSS is validated through a proof-of-concept implementation based on a real-world laboratory case study. This demonstrates its practical applicability and robustness in complex industrial environments.

Digital twin development of industrial cyber-physical systems requires modeling, simulation, and monitoring to provide accurate digital replicas mimicking system dynamics. To this aim, modern solutions employ data-driven approaches to capture normal system dynamics using historical data, and anomaly detection using run-time data. However, these approaches are typically process-agnostic and have limited explainability, which negatively impacts trustworthiness. Hence, we propose a novel framework for the digital twin development of industrial cyber-physical systems based on process mining, which connects data-driven and process-based analyses. The framework implements an offline phase that systematically identifies the most suitable process model to capture normal dynamics and simulates faulty dynamics for what-if analysis. The subsequent online phase involves run-time monitoring for anomaly detection. We develop a proof-of-concept application of the framework addressing a water distribution case study that allows physical fault injection. Results highlight the importance of adopting high-quality process models to significantly improve anomaly detection and time performance.

In the context of multicriteria decision analysis , Pairwise Comparison Matrices (PCMs) represent a widely-used mathematical structure to capture comparisons about criteria or alternatives. When a confidence (or uncertainty) level is associated to the provided comparison measures, PCMs are naturally extended to the so-called Interval Pairwise Comparison Matrices (IPCM). Classical approaches, based on the eigenvector associated to the largest eigenvalue of the matrix (Analytic Hierarchy Process) or optimization problems (e.g. the Logarithmic Least Squares approach), are frequently adopted in order to estimate absolute utilities from relative judgements in absence of expert's uncertainty. Conversely, in the presence of uncertain measures, computationally intensive approaches based on Monte Carlo analysis have been adopted. In this paper, starting from a given PCM, we provide an approach able to find the smallest multiplicative perturbations of its elements able to perturb the obtained ranking from the ordinal perspective. On the basis of such approach, we provide theoretical results which can be adopted also in the case of uncertain measures (e.g., IPCMs) with the aim to characterize the stability of the obtained ranking on the basis of the given uncertainty. As testified by numerical examples that conclude the paper, the proposed framework can greatly simplify the classical approaches by providing the possibility to make direct assumptions on the final outcome of Monte Carlo analysis and reducing or avoiding its required computational effort.

In the last decade, many efforts have been devoted to indoor localization and positioning. In this paper, a hybrid indoor localization system has been developed within the European project REFIRE for emergency situations. The REFIRE solution estimates the user's pose according to a prediction-correction scheme. The user is equipped with a waist-mounted inertial measurement unit and a radio frequency identification (RFID) reader. In the correction phase, the estimation is updated by means of geo-referenced information fetched from passive RFID tags predeployed into the environment. Accurate position correction is obtained through a deep analysis of the RFID system radiation patterns. To this end, extensive experimental trials have been performed to assess the RFID system performance, both in static and dynamic operating conditions. Experimental validation in realistic environments shows the effectiveness of the proposed indoor localization system, even during long-last missions and/or using a limited number of tags.

In this paper we develop a novel approach to identify the best policy to allocate protection resources to raise the overall network robustness to node disruption. The proposed methodology is based on the identification of the most critical elements of the network in terms of their connectivity contribution to the entire system. The definition of the critical nodes is performed as a game-theoretic analysis based on Shapley Value theory. We validate the proposed approach with respect to a case study featuring a social network; by comparison with state of the art metrics, we experimentally show that the proposed protection strategy is particularly effective in preserving the residual network connectivity.

In this paper, we provide a novel framework to assess the vulnerability/robustness of a network with respect to pair-wise nodes' connectivity. In particular, we consider attackers that aim, at the same time, at dealing the maximum possible damage to the network in terms of the residual connectivity after the attack and at keeping the cost of the attack (e.g., the number of attacked nodes) at a minimum. Differently from the previous literature, we consider the attacker perspective using a multiobjective formulation and, rather than making hypotheses on the mindset of the attacker in terms of a particular tradeoff between the objectives, we consider the entire Pareto front of nondominated solutions. Based on that, we define novel global and local robustness/vulnerability indicators and we show that such indices can be the base for the implementation of effective protection strategies. Specifically, we propose two different problem formulations and we assess their performances. We conclude this paper by analyzing, as case studies, the IEEE118 power network and the U.S. Airline Network as it was in 1997, comparing the proposed approach against centrality measures.

The widespread diffusion of the Internet of Things (IoT) is introducing a huge number of Internet-connected devices in our daily life. Mainly, wearable devices are going to have a large impact on our lifestyle, especially in a healthcare scenario. In this framework, it is fundamental to secure exchanged information between these devices. Among other factors, it is important to take into account the link between a wearable device and a smart unit (e.g., smartphone). This connection is generally obtained via specific wireless protocols such as Bluetooth Low Energy (BLE): the main topic of this work is to analyse the security of this communication link. In this paper we expose, via an experimental campaign, a methodology to perform a vulnerability assessment (VA) on wearable devices communicating with a smartphone. In this way, we identify several security issues in a set of commercial wearable devices.

Localization and tracking are fundamental features in emergency response operations, where the mission leader needs to be aware of the team location. This paper addresses the localization for rescuers by exploiting wireless sensor networks embedded in the environment. Specifically, a pre-deployed network is considered and a localization algorithm is designed to find the location of the node and to track the rescuers cooperatively. Nodes estimate their own positions, while rescuers improve and augment their location awareness during mission by navigating across via points suggested by the network, thus improving the overall localizability. Experimental results show the effectiveness of the approach.

Distance-based network localization is a very effective way to obtain the position of nodes in a network via range measuring; this, however, requires nodes with nontrivial sensing capabilities. Range-free techniques, conversely, use much simpler nodes but often provide just a rough estimate on the node's position. In this paper we develop a hybrid localization algorithm that combines range-free and distance-based sensors. A detailed simulation campaign aimed at corroborating the findings and at showing the potentialities of the approach concludes the paper.

In the field of reliability engineering, several approaches have been developed to identify those components that are important to the operation of the larger interconnected system. We extend the concept of component importance measures to the study of industry criticality in a larger system of economically interdependent industry sectors that are perturbed when underlying infrastructures are disrupted. We provide measures of (i) those industries that are most vulnerable to disruptions and (ii) those industries that are most influential to cause interdependent disruptions. However, difficulties arise in the identification of critical industries when uncertainties exist in describing the relationships among sectors. This work adopts fuzzy measures to develop criticality indices, and we offer an approach to rank industries according to these fuzzy indices. Much like decision makers with the knowledge of the most critical components in a physical system, the identification of these critical industries provides decision makers with priorities for resources. We illustrate our approach with an interdependency model driven by US Bureau of Economic Analysis data to describe industry interconnectedness.

Localization and tracking support is useful in many contexts and becomes crucial in emergency response scenarios: being aware of team location is one of the most important knowledge for incident commander. In this work both localization and tracking for rescuers are addressed in the framework of REFIRE project. The designed positioning system is based on the well-known prediction-correction schema adopted in field robotics. Proprioceptive sensors, i.e., inertial sensors and magnetometer, mounted on the waist of the rescuers, are used to form a coarse estimation of the locations. Due to the drift of inertial sensors, the position estimate needs to be updated by exteroceptive sensors, i.e., RFID system composed by tags embedded in the emergency signs as exteroceptive sensors and a wearable tag-reader. In long-lasting mission RFID tags reset the drift by providing a positioning having room-level accuracy.

This paper deals with a new approach to multi robot localization. An Interlaced extended Kalman filter is shown to be a good solution to the problem of estimating the pose of a team of robots with a fully decentralized algorithm. Moreover, it is feasible to dynamically "correct" the estimation autonomously evaluated by each single robot, updating this quantity anytime two robots randomly come across. The algorithm combines the robustness of a full state EKF with the simplicity of its interlaced implementation. It does not need global supervision, and allows a large flexibility in using exteroceptive sensors. The paper presents some simulations to show the feasibility of the approach considering a set of robots equipped with different combinations of sensors and with wireless communication devices able to support data exchange when they are sufficiently close

This paper investigates the implementation of a wireless mobile ad-hoc network to guarantee that an autonomously driven mobile vehicle remains connected to a limited-coverage base antenna during its motion. To the purpose, the use of a platoon of mobile robots is proposed to carry a number of repeater antennas; these must be suitably moved to dynamically ensure a multi-hop communication link to the vehicle that extends outside the area covered by the sole base antenna. Self configuration of the robots' platoon is then achieved by a singularity-robust task-priority inverse kinematics algorithm via the definition of suitable task functions. The obtained simulation results show the effectiveness of the proposed approach.

Real-time applications ask for reduced computational cost algorithms. In robotic exploration of unstructured environments the problem is more challenging: several tasks, at the same time, must be carried on ranging from reactive behaviours to the building of a structured representation of the environment itself. Many sensor signals have to be processed at each step to estimate both landmarks and robot positions. This mapping aptitude can be implemented through an extended Kalman filter recently proposed in a previous paper. Due to the large number of estimated variables, and real-time constraints, the filter is better implemented in its interlaced version. The novelty of this paper consists in extending the IEKF filter, removing some hypothesis on the linearity of both state transition and observation mapping, in order to further reduce computational burden and then achieve a better tradeoff among computational load and accuracy.

Performing the requirements engineering process for software-intensive systems, characterized by a high organizational impact, requires the analysts to handle a large amount of information, related to desires, needs, and constraints of a vast number of completely different stakeholders. The paper presents an organization modeling-based requirements engineering framework, where advanced requirements engineering techniques are combined with software quality modelling approaches to support the analysts in capturing, and formalising the knowledge embedded in the organisation, from which the sought system functionality and quality attributes may be derived. The collected knowledge will not only support the specific project but will represent an organizational assetto be exploited for future systems/organization evolution. As a case study, the paper reports on an on-going project concerned with the development and introduction, within the Italian Office of the Prime Minister, of a Electronic Record Management System, as a first step towards a paperless knowledge workplace.