This paper investigates equilibrium strategies in networked control systems subject to false data injection (FDI) attacks, employing a game-theoretic approach. Our framework characterizes the dynamics that revolves around the freshness of the data from multiple sensors using the age of incorrect information (AoII) metric. The interaction between legitimate transmitters, aiming to minimize the system AoII and their transmission costs, and a malicious adversary, aiming to maximize AoII while managing FDI costs, is modeled as a non-cooperative game. We analytically demonstrate the existence and uniqueness of a Nash equilibrium (NE) and derive explicit conditions characterizing equilibrium resource allocation strategies. We also present examples of applications related to secure healthcare and automotive control. The numerical results validate our theoretical findings, highlighting the strategic impact of the system parameters, including drift rates, FDI and transmission costs, and resource constraints. Our analysis yields actionable guidelines for enhancing sensor security through parameter tuning and resource allocation.

We study a control system where sensor measurements are transmitted to a remote station. Information may become outdated due to system drift or compromised by malicious false data injection. To quantify the impact of staleness and inaccuracy in the information at the receiver’s side, we use Age of Incorrect Information (AoII). In particular, we consider the Excess AoII above a certain threshold as our key objective to minimize, which we argue to be a sensible goal for many real-time control systems. We adopt a game-theoretic framework to model the strategic interaction between a transmitter, which aims to minimize both Excess AoII and transmission costs, and a malicious agent, which seeks to maximize the same Excess AoII metric while minimizing its own costs. Our analysis reveals the existence of a Nash equilibrium for this game, and we investigate how the system parameters influence the adversary’s decision to attack, identifying the conditions under which an attack becomes advantageous or not.

Critical Infrastructures (CIs) such as power grid, water and gas distribution are controlled by Industrial Control Systems (ICS). Sensors and actuators of a physical plant are managed by the ICS. Data and commands transmitted over the network from the Programmable Logic Controllers (PLCs) are saved and parsed within the Historian. Generally, this architecture guarantees to check for any process anomalies that may occur due to component failures and cyber attacks. The other use of this data allows activities such as forensic analysis. To secure the network is also crucial to protect the communication between devices. A cyber attack on the log devices could jeopardize any forensic analysis be it for maintenance, or discovering an attack trail. In this paper is proposed a strategy to secure plant operational data recorded in the Historian and data exchange in the network. An integrity checking mechanism, in combination with blockchain, is used to ensure data integrity. Data redundancy is achieved by applying an efficient replication mechanism and enables data recovery after an attack.

Over the last year, the Internet of Things (IoT) drove the development of cyber-physical systems, leading the convergence between information and operational technologies. This coupling improves performances and saves costs but increases the number of vulnerabilities and the attack surface to malicious actors. Consequently, it is mandatory to understand how IoT devices can be properly integrated into a more secure environment. This issue is even more crucial in the field of Critical Infrastructures, such as energy, water, transportation, and telecommunications. To this aim, this paper analyzes the concept of opacity for Discrete Event Systems (DES) and applies it to a real system. The opacity describes the ability of the system to keep some states secret even if an attacker knows the happening of some events. The proposed approach is validated by applying the concept of opacity on the analysis over a simple system. It is demonstrated that opacity can be guaranteed and exploited when systems having different levels of security are integrated.

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.

Industrial Control systems traditionally achieved security by using isolation from the outside and proprietary protocols to communicate inside. This paradigm is changed with the advent of the Industrial Internet of Things that foresees flexible and interconnected systems. In this contribution, the threats coming from this new approach are analyzed and a framework for identify them is proposed. It is based on the common signature based intrusion detection system developed in the information technology domain, however, to cope with the constraints of the operation technology domain, it exploits anomaly based features. Specifically, it is able to analyze the traffic on the network at application layer by mean of deep packet inspection, parsing the information carried by the proprietary protocols. Two different topologies are adopted to cope also with legacy systems. A simple set up is considered to prove the effectiveness of the approach.

Programmable Logic Controller (PLC) technology plays an important role in the automation architectures of several critical infrastructures such as Industrial Control Systems (ICS), controlling equipment in contexts such as chemical processes, factory lines, power production plants or power distribution grids, just to mention a few examples. Despite their importance, PLCs constitute one of the weakest links in ICS security, frequently due to reasons such as the absence of secure communication mechanisms, authenticated access or system integrity checks. While events such as the Stuxnet worm have raised awareness for this problem, industry has slowly reacted, either due to reliability or cost concerns. This paper introduces the Shadow Security Unit, a low-cost device deployed in parallel with a PLC or Remote Terminal Unit (RTU), being capable of transparently intercepting its communications control channels and physical process I/O lines to continuously assess its security and operational status. The proposed device does not require significant changes to the existing control network, being able to work in standalone or integrated within an ICS protection framework.

This paper introduces and characterizes the Baxter Easyhand, a new 3D printed hand derived from the Yale T42 hand [5], [10], but designed specifically to be mounted on the Baxter robot from Rethink robotics. Because this hand is designed specifically for Baxter, we are able to make some important simplifications in the design relative to other 3D printed hands. In particular, the Easyhand is smaller than most other 3D printed hands and it is powered by the native Baxter gripper actuator. As a result, our hand is cheaper, lighter, and easier to interface with than other robot hands available for Baxter. This paper details the design of the hand and its mechanical characteristics and reports results from experiments that characterize its grasping performance.

This paper focuses on the extension of the transferable belief model (TBM) to a multiagent-distributed context where no central aggregation unit is available and the information can be exchanged only locally among agents. In this framework, agents are assumed to be independent reliable sources which collect data and collaborate to reach a common knowledge about an event of interest. Two different scenarios are considered: In the first one, agents are supposed to provide observations which do not change over time (static scenario), while in the second one agents are assumed to dynamically gather data over time (dynamic scenario). A protocol for distributed data aggregation, which is proved to converge to the basic belief assignment given by an equivalent centralized aggregation schema based on the TBM, is provided. Since multiagent systems represent an ideal abstraction of actual networks of mobile robots or sensor nodes, which are envisioned to perform the most various kind of tasks, we believe that the proposed protocol paves the way to the application of the TBM in important engineering fields such as multirobot systems or sensor networks, where the distributed collaboration among players is a critical and yet crucial aspect.

The aim of this work is an investigation on the effects of networks topology to spatially-structured evolutionary algorithms' dynamics. We applied the algorithm on a multi-modal optimization problem and we focused our study on convergence time and diversity of the solutions. Using as algorithms' underlying structure different network models we studied the relationship between algorithm dynamic, i.e. convergence time, first hitting time and number of distinct optima found during the evolution, and networks' characteristics. A comparison with a panmictic evolutionary algorithm is made to study the effects of the introduction of a structure in the mating dynamics, resulting in an enhancement of diversity and containing the convergence time and first hitting time overhead. The results on the proposed multi-modal combinatorial optimization problem using regular graphs and Watts-Strogatz networks show that the underlying network characteristics clearly influences algorithm dynamics and diversity of the solutions found.

In the last few years critical infrastructures have become more and more tightly interconnected and their protection is one of the major issues for the national and international security. In order to achieve that, many modeling techniques for the interdependencies existing among them have been proposed. Now, a crucial issue is how to use such models to develop tools able to estimate the status of key elements of CIs, quantify the possible threats and suggest adequate countermeasures to human operators and actors. Due to security, commercial and technological aspects, the only feasible approach is to provide distributed and interconnected state/interdependency estimators. In this paper the general problem of the state estimation of interconnected systems sharing the same model is introduced. A first step in the solution of such a challenging problem is then provided in the case of linear systems. The final objective of this research is to define an effective framework for the problem at hand, and then implement and validate an on-line distributed state/interdependency estimator within the EU IST MICIE project.

In this paper, a novel genetic algorithm based on a ¿collaborative¿ fitness-sharing technique to deal with the Multi-Robot Localization problem is proposed. Indeed, the use of the fitness-sharing is twofold and competitive. It preserves the diversity among individuals during the space exploration process, thus maintaining evolutionary niches over time, and reinforces the best hypotheses by means of collaboration among robots, thus augmenting the selection pressure. Simulations by exploiting the robotics framework Player/Stage have been performed along with a proper statistical analysis for performance assessment.

Localisation is one of the most important tasks to be accomplished in order to realize the complete autonomy of a mobile robot. In this paper, a new strategy for global localisation is proposed. Applying this method a robot is able to safely initialise its position or relocalise itself in case of recovery of pose tracking failure. The algorithm presented adopts a hybrid approach. First a particle filter is used to generate hypotheses on the possible pose supposing that no movements are allowed to avoid collisions. Thereafter safe trajectories are planned and executed to reduce the remaining ambiguities while the hypotheses are monitored and validated by a set of parallel extended Kalman filters. The novelty of this approach stands on the ability to generate the pose hypotheses without any feature-based knowledge. As a consequence, a landmark-based description of the environment is no longer required for the algorithm execution.

One of the most important problems in mobile robotics is to realize the complete robot's autonomy. In order to achieve this goal several tasks have to be accomplished. Among them, the robot's ability to localise itself turns out to be critical. The research community has provided, through the years, different methodologies to face the localisation problem, such as the Kalman filter or the Monte Carlo Integrations methods. In this paper a different approach relying on a specialisation of the genetic algorithms is proposed. The novelty of this approach is to take advantage of the complex networks theory for the spatial deployment of the population to more quickly find out the optimal solutions. In fact, modelling the search space with complex networks and exploiting their typical connectivity properties, results in a more effective exploration of such space.

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

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.

We consider the inverse dynamics problem for robot arms with flexible links, i.e., the computation of the input torque that allows exact tracking of a trajectory defined for the manipulator end-effector. A stable inversion controller is derived numerically, based on the computation of bounded link deformations and, from these, of the required feedforward torque associated with the desired tip motion. For a general class of multi-link flexible manipulators, three alternative computational algorithms are presented, all defined on the second-order robot dynamic equations. Trajectory tracking is obtained by adding a (partial) state feedback, within a nonlinear regulation approach. Experimental results are reported for the FLEXARM robot.

In this paper we present a novel finite dimensional learning algorithm for end point trajectory control of a flexible link moving on a horizontal plane. Given a desired end point trajectory generated by a finite dimensional exosystem, by means of successive trials, the algorithm proposed learns the initial state and the control needed to exactly track the desired trajectory. An internal stability constraint is also enforced, which prevents undesirable excessive vibrations and control effort. Initialization of the flexible link state is automatically accomplished as a part of the same learning process. Algorithm ability of fully rejecting linear plant perturbations and disturbances which are linear functions of the exosystem state is formally proven. Experimental results show the effectiveness of the algorithm introduced and its robustness with respect to nonlinear plant perturbations and unmodeled dynamics.

We present an iterative learning controller for nonholonomic systems in chained form. The learning algorithm relies on the fact that chained-form systems are linear under piecewise-constant inputs. The proposed control scheme requires the execution of a small number of experiments in order to drive the system to the desired state in finite time, with nice convergence and robustness properties with respect to modeling inaccuracies as well as disturbances. As a case study, a car-like wheeled mobile robot is considered. Both simulation and experimental results are reported in order to show the performance of the proposed method.

Experimental results on the repositioning of a rigid robot link from one equilibrium point to another are presented. By means of a learning algorithm, which uses only the positioning error at the end of a trial, the control torque needed to move the robot is found. After a high gain feedback linearization of the robot dynamics, a simple, finite dimensional learning algorithm is applied. The results of the experiments, carried out on a two link robot under gravity, show that the algorithm is robust and performs quite satisfactorily.>