Cyber-Physical Systems (CPSs) are a large class of systems characterized by networked co-operating sub-systems, that perceive surrounding environment via sensors and actuators. Cybersecurity is relevant in CPSs because, on the one hand, these systems expose a wide cyber-attack surface while, on the other hand, a security infringement may translate into a safety infringement. This work presents a methodology for developing an intrusion detection system for CPSs based on neural networks. The methodology exploits a digital twin of the CPS to generate traces of executions. An instrumented approach is used to extend the digital twin model by introducing functions that simulate the effects of various class of attacks on the system. The instrumented digital twin is used to gather data of the system’s behaviour with and without attacks. Collected data are used for training the neural network. To illustrate the methodology we consider a case-study featuring an Adaptive Cruise Control System in autonomously driving vehicles. A Multi-Layer Perceptron neural network is trained to detect attacks to sensors. Results show an high accuracy in detecting attacks.

Abstract Unmanned aerial vehicle (UAV) co-operative systems are complex cyber-physical systems that integrate a high-level control algorithm with pre-existing closed implementations of lower-level vehicle kinematics. In model-driven development, simulation is one of the techniques that are usually applied, together with testing, in the analysis of system behaviours. This work proposes a method and tools to validate the design of UAV co-operative systems based on co-simulation and formal verification. The method uses the Prototype Verification System, an interactive theorem prover based on a higher-order logic language, and the Functional Mock-up Interface, a widely accepted standard for co-simulation. In this paper, results on the co-simulation and proofs of safety requirements of a representative co-ordination algorithm are shown and discussed in a scenario where quadcopters are deployed and perform space-coverage operations.

This paper presents an approach to create high-fidelity models suited for digital twin application of distributed multi-agent cyber–physical systems (CPSs) exploiting the combination of simulation units through co-simulation. This approach allows for managing the complexity of cyber–physical systems by decomposing them into multiple intertwined components tailored to specific domains. The native modular design simplifies the building, testing, prototyping, and extending CPSs compared to monolithic simulator approaches. A system of platoon of vehicles is used as a case study to show the advantages achieved with the proposed approach. Multiple components model the physical dynamics, the communication network and protocol, as well as different control software and external environmental situations. The model of the platooning system is used to compare the performance of Vehicle-to-Vehicle communication against a centralized multi-access edge computing paradigm. Moreover, exploiting the detailed model of vehicle dynamics, different road surface conditions are considered to evaluate the performance of the platooning system. Finally, taking advantage of the co-simulation approach, a solution to drive a platoon in critical road conditions has been proposed. The paper shows how co-simulation and design space exploration can be used for parameter calibration and the design of countermeasures to unsafe situations.

Cyber-physical systems (CPSs) are systems in which digital and physical entities cooperate. When CPSs execute safety-critical tasks, monitoring of their behavior is of main concern. The CPS should be moved to a safe state in case of malfunctions. This demo shows how an on-line supervisor can be developed for a Line Follower Robot, starting from a digital model of the robot.

This paper presents an approach to create high fidelity Digital-Twin models for distributed multi-agent cyber-physical systems based on the combination of simulating components, generated from different modeling languages, each tailored for the specific domain of the subsystem. The approach specifically addresses the wireless communication domain, exploiting a Python module as a simulating component to evaluate the impact of network delay among the distributed elements of the system under analysis. A case study with a platoon of four vehicles following a leading car, all modeled in Simulink, is used to show the applicability of the approach, allowing the comparison between a Vehicle-to-Vehicle communication against a centralized multi-access edge computing paradigm.

Wireless Sensor Networks (WSNs) are particularly prone to security attacks. However, it is well-known that perfect security is not achievable. Therefore, it is important to identify threats and evaluate their severity, for prioritizing the security countermeasures to be adopted, even since design time. In this work, we propose an approach that binds formal methods and network simulation for assessing the effects of security attacks on WSN applications from design time, starting from the abstract model of the system. Formal methods make it possible to build abstract system models and state properties of general validity, but cannot provide any concrete measurement regarding the network and the application. On the other hand, network simulators can provide precise and realistic information about simulated scenarios only. As a proof of concept, we design and prototype an application-level communication protocol, which is simulated both on attack free and attack scenarios. First, the protocol's formal properties are specified and proved via UPPAAL. Then, the resulting UPPAAL model is used to automatically generate a network model for the WSN simulator Castalia. Finally, the network model is simulated against attack free and attack scenarios, for gathering realistic information about the protocol behavior and performance.

This paper presents a methodology for the formal modeling of security attacks on cyber-physical systems, and the analysis of their effects on the system using logic theories. We consider attacks only on sensors and actuators. A simulated attack can be triggered internally by the simulation algorithm or interactively by the user, and the effect of the attack is a set of assignments to the variables. The effects of the attacks are studied by injecting attacks in the system model and simulating them. The overall system, including the attacks, the system dynamics and the control part, is co-simulated. The INTO-CPS framework has been used for co-simulation, and the methodology is applied to the Line follower robot case study of the INTO-CPS project.

This paper presents a method to check data secure flow in security annotated AUTOSAR models. The approach is based on information flow analysis and abstract interpretation. The analysis computes the lowest security level of data sent on a communication, according to the annotations in the model and the code of runnables. An abstract interpreter executes runnables on abstract domains that abstract from real values and consider only data dependency levels. Data secure flow is verified if data sent on a communication always satisfy the security annotation in the model. The work has been developed in the EU project Safure, where modeling extensions to AUTOSAR have been proposed to improve security in automotive communications.