Redundancy is widely used as a method for fault and intrusion tolerance. However, if the redundant components lack sufficient diversity, potentially dangerous common mode failures may go undetected. To address this issue, the design diversity approach has been proposed in the literature for decades. In this article, we take an innovative approach to this problem by introducing a broader notion of diversity, which leverages Metamorphic Relations (MRs), i.e., necessary properties that must hold among diverse inputs and diverse outputs. We define two generic categories of MRs that establish data diversity and functional diversity. Furthermore, we elaborate on two corresponding logical architectures, paying particular attention to the necessary conditions for the adjudicator component. Finally, we present an initial evaluation of the proposed architectures, which points out the advantages with respect to their counterparts based on the traditional design diversity method, and discuss future research directions for this novel conceptual approach to redundancy-based fault/intrusion tolerance.

To cope with unforeseen attacks to software systems in critical application domains, redundancy-based ITSs schemes are among popular countermeasures to deploy. Designing the adequate ITS for the stated security requirements calls for stochastic analysis supports, able to assess the impact of variety of attack patterns on different ITS configurations. As contribution to this purpose, a stochastic model for ITS is proposed, whose novel aspects are the ability to account for both camouflaging components and for correlation aspects between the security failures affecting the diverse implementations of the software cyber protections adopted in the ITS. Extensive analyses are conducted to show the applicability of the model; the obtained results allow to understand the limits and strengths of selected ITS configurations when subject to attacks occurring in unfavorable conditions for the defender.

A new methodology for effective definition and efficient evaluation of dependability-related properties is proposed. The analysis targets the systems composed of a large number of components, each one modeled implicitly through high-level formalisms, such as stochastic Petri nets. Since the component models are implicit, the reward structure that characterizes the dependability properties has to be implicit as well. Therefore, we present a new formalism to specify those reward structures. The focus here is on component models that can be mapped to stochastic automata with one or several absorbing states so that the system model can be mapped to a stochastic automata network with one or several absorbing states. Correspondingly, the new reward structure defined on each component's model is mapped to a reward vector so that the dependability-related properties of the system are expressed through a newly introduced measure defined starting from those reward vectors. A simple, yet representative, case study is adopted to show the feasibility of the method.

A technique to approximate solution bundles, i.e., solutions of a parametric model where parameters are treated as independent variables instead of constants, is presented for Markov models. Analyses based on an approximated solution bundle are more efficient than those that solve the model for all combinations of parameters’ values separately. In this paper the idea is to properly adapt low rank tensor approximation techniques, and in particular Adaptive Cross Approximation, to the evaluation of performability attributes. Application on exemplary case studies confirms the advantages of the new solution technique with respect to solving the model for all time and parameters’ combinations.

Given their crucial role for a society and economy, an essential component of critical infrastructures is the Bad State Estimator (BSE), responsible for detecting malfunctions affecting elements of the physical infrastructure. In the past, the BSE has been conceived to mainly cope with accidental faults, under assumptions characterizing their occurrence. However, evolution of the addressed systems category consisting in pervasiveness of ICT-based control towards increasing smartness, paired with the openness of the operational environment, contributed to expose critical infrastructures to intentional attacks, e.g. exploited through False Data Injection (FDI). In the flow of studies focusing on enhancements of the traditional BSE to account for FDI attacks, this paper proposes a new solution that introduces randomness elements in the diagnosis process, to improve detection abilities and mitigate potentially catastrophic common-mode errors. Differently from existing alternatives, the strength of this new technique is that it does not require any additional components or alternative source of information with respect to the classic BSE. Numerical experiments conducted on two IEEE transmission grid tests, taken as representative use cases, show the applicability and benefits of the new solution.

Traditionally, critical infrastructures demand for high dependability, being the services they provide essential to human beings and the society at large. However, more recent attention to cautious usage of energy resources is changing this vision and calls for solutions accounting for appropriate multi-requirements combinations when developing a critical infrastructure. In such a context, analysis supports able to assist the designer in envisioning a satisfactory trade-off among the multi-requirements for the system at hand are highly helpful. In this paper, the focus is on the railway sector and the contribution is a stochastic model-based analysis framework to quantitatively assess trade-offs between dependability indicators and electrical energy consumption incurred by the rail switch heating system.Moving from a preliminary study that concentrated on energy consumption only, the analysis framework has been extended to become a solid support to devise appropriate tuning of the heating policy that guarantees satisfactory trade-offs between dependability and energy consumption. An evaluation campaign in a variety of climate scenarios demonstrates the feasibility and utility of the developed framework.

Railways monitoring and control are currently performed by different heterogeneous vertical systems working in isolation without or with limited cooperation among them. Such configuration, widely adopted in practical deployments today, is in contrast with the integrated vision of systems that are at the foundation of the smart-city concept. In order to overcome the current fractured ecosystem that monitors and controls railways functionalities, the adoption of a novel integrated approach is mandatory to create an all-in-one railway system. To this aim, new IoT-based communication technologies, like wireless or Power Line Communication technologies, are considered the main enablers to integrate in a very rapid and easy manner existing vertical systems. In this work, we analyse the architecture of future railways systems based on a mix of wireless and Power Line Communication technologies. In our analysis, we aim at studying possible failure management strategies on rail-road switches to improve the level of reliability, crucial requirement for systems that demand maximum resiliency as they manage a critical function of the infrastructure. In particular, we propose a set of solutions aimed at detecting and handling network and sensor failures to ensure continuity in the execution of the basic control functions. The proposed approach is evaluated by means of simulations and demonstrated to be effective in ensuring a good level of performance even when failures occur.

Modeling cyber-physical systems (CPSs) for assessment or design support purposes is a complex activity. Capturing all relevant physical, structural or behavioral aspects of the system at hand is a crucial task, which often implies representation of peculiar features/constraints through non-linear equations. Values that fulfill the constraints, described with a domain specific language, are obtained solving the equations through a properly developed solution tool. Only for a limited set of CPSs it is possible to find a straightforward strategy to design the software that solves the constraints equations. In the general case, instead, the modeler has to develop an ad-hoc artifact for each different system. This is the case of non-holomorphic but real analytic complex equations, adopted to represent system components with wave behaviors. In this paper, we present a new approach to develop a software for solving such complex equations following a generative programming strategy, based on Wirtinger derivatives within the Newton-Raphson method.

This paper addresses the generation of stochastic models for dependability and performability analysis of complex systems, through automatic replication of template models. The proposed solution is tailored to systems composed by large populations of similar non-anonymous components, interconnected with each other according to a variety of topologies. A new efficient replication technique is presented and its implementation is discussed. The goal is to improve the performance of simulation solvers with respect to standard approaches, when employed in the modeling of the addressed class of systems, in particular for loosely interconnected system components (as typically encountered in the electrical or transportation sectors). Effectiveness of the new technique is demonstrated by comparison with a state of the art alternative solution on a representative case study.