Embedded electronics are widely employed in cyber-physical systems (CPSes), which tightly integrate and coordinate computational and physical elements. CPSes are extensively deployed in security-critical applications and nationwide infrastructure. Perimeter security approaches to preventing malware infiltration of CPSes are challenged by the complexity of modern embedded systems incorporating numerous heterogeneous and updatable components. Global supply chains and third-party hardware components, tools, and software limit the reach of design verification techniques and introduce security concerns about deliberate Trojan inclusions. As a consequence, skilled attacks against CPSes have demonstrated that these systems can be surreptitiously compromised. Existing run-time security approaches are not adequate to counter such threats because of either the impact on performance and cost, lack of scalability and generality, trust needed in global third parties, or significant changes required to the design flow. We present a protection scheme called Run-time Enhancement of Trusted Computing (RETC) to enhance trust in CPSes containing untrusted software and hardware. RETC is complementary to design-time verification approaches and serves as a last line of defense against the rising number of inexorable threats against CPSes. We target systems built using reconfigurable hardware to meet the flexibility and high-performance requirements of modern security protections. Security policies are derived from the system physical characteristics and component operational specifications and translated into synthesizable hardware integrated into specific interfaces on a per-module or per-function basis. The policy-based approach addresses many security challenges by decoupling policies from system-specific implementations and optimizations, and minimizes changes required to the design flow. Interface guards enable in-line monitoring and enforcement of critical system computations at run-time. Trust is only required in a small set of simple, self-contained, and verifiable guard components. Hardware trust anchors simultaneously addresses the performance, flexibility, developer productivity, and security requirements of contemporary CPSes. We apply RETC to several CPSes having common security challenges including: secure reconfiguration control in reconfigurable cognitive radio platforms, tolerating hardware Trojan threats in third-party IP cores, and preserving stability in process control systems. High-level architectures demonstrated with prototypes are presented for the selected applications. Implementation results illustrate the RETC efficiency in terms of the performance and overheads of the hardware trust anchors. Testbenches associated with the addressed threat models are generated and experimentally validated on reconfigurable platform to establish the protection scheme efficacy in thwarting the selected threats. This new approach significantly enhances trust in CPSes containing untrusted components without sacrificing cost and performance. / Ph. D.

Cyber-physical systems (CPSs) are systems that integrate physical elements with a cyber layer that enables sensing, monitoring, and processing the data from the physical components. Examples of CPSs include autonomous vehicles, unmanned aerial vehicles (UAVs), smart grids, and the Internet of Things (IoT). In particular, many critical infrastructure (CI) that are vital to our modern day cities and communities, are CPSs. This wide range of CPSs domains represents a cornerstone of smart cities in which various CPSs are connected to provide efficient services. However, this level of connectivity has brought forward new security challenges and has left CPSs vulnerable to many cyber-physical attacks and disruptive events that can utilize the cyber layer to cause damage to both cyber and physical components. Addressing these security and operation challenges requires developing new security solutions to prevent and mitigate the effects of cyber and physical attacks as well as improving the CPSs response in face of disruptive events, which is known as the CPS resilience. To this end, the primary goal of this dissertation is to develop novel analytical tools that can be used to study, analyze, and optimize the resilience and security of critical CPSs. In particular, this dissertation presents a number of key contributions that pertain to the security and the resilience of multiple CPSs that include power systems, the Internet of Things (IoT), UAVs, and transportation networks. First, a mathematical framework is proposed to analyze and mitigate the effects of GPS spoofing attacks against UAVs. The proposed framework uses system dynamics to model the optimal routes which UAVs can follow in normal operations and under GPS spoofing attacks. A countermeasure mechanism, built on the premise of cooperative localization, is then developed to mitigate the effects of these GPS spoofing attacks. To practically deploy the proposed defense mechanism, a dynamic Stackelberg game is formulated to model the interactions between a GPS spoofer and a drone operator. The equilibrium strategies of the game are analytically characterized and studied through a novel, computationally efficient algorithm. Simulation results show that, when combined with the Stackelberg strategies, the proposed defense mechanism will outperform baseline strategy selection techniques in terms of reducing the possibility of UAV capture. Next, a game-theoretic framework is developed to model a novel moving target defense (MTD) mechanism that enables CPSs to randomize their configurations to proactive deter impending attacks. By adopting an MTD approach, a CPS can enhance its security against potential attacks by increasing the uncertainty on the attacker. The equilibrium of the developed single-controller, stochastic MTD game is then analyzed. Simulation results show that the proposed framework can significantly improve the overall utility of the defender. Third, the concept of MTD is coupled with new cryptographic algorithms for enhancing the security of an mHealth Internet of Things (IoT) system. In particular, using a combination of theory and implementation, a framework is introduced to enable the IoT devices to update their cryptographic keys locally to eliminate the risk of being revealed while they are shared. Considering the resilience of CPSs, a novel framework for analyzing the component- and system-level resilience of CIs is proposed. This framework brings together new ideas from Bayesian networks and contract theory – a Nobel prize winning theory – to define a concrete system-level resilience index for CIs and to optimize the allocation of resources, such as redundant components, monitoring devices, or UAVs to help those CIs improve their resilience. In particular, the developed resilience index is able to account for the effect of CI components on the its probability of failure. Meanwhile, using contract theory, a comprehensive resource allocation framework is proposed enabling the system operator to optimally allocate resources to each individual CI based on its economic contribution to the entire system. Simulation results show that the system operator can economically benefit from allocating the resources while dams can have a significant improvement in their resilience indices. Subsequently, the developed contract-theoretic framework is extended to account for cases of asymmetric information in which the system operator has only partial information about the CIs being in some vulnerability and criticality levels. Under such asymmetry, it is shown that the proposed approach maximizes the system operator's utility while ensuring that no CI has an incentive to ask for another contract. Next, a proof-of-concept framework is introduced to analyze and improve the resilience of transportation networks against flooding. The effect of flooding on road capacities and on the free-flow travel time, is considered for different rain intensities and roads preparedness. Meanwhile, the total system's travel time before and after flooding is evaluated using the concept of a Wardrop equilibrium. To this end, a proactive mechanism is developed to reduce the system's travel time, after flooding, by shifting capacities (available lanes) between same road sides. In a nutshell, this dissertation provides a suite of analytical techniques that allow the optimization of security and resilience across multiple CPSs. / Doctor of Philosophy / Cyber-physical systems (CPSs) have recently been used in many application domains because of their ability to integrate physical elements with a cyber layer allowing for sensing, monitoring, and remote controlling. This pervasive use of CPSs in different applications has brought forward new security challenges and threats. Malicious attacks can now leverage the connectivity of the cyber layer to launch remote attacks and cause damage to the physical components. Taking these threats into consideration, it became imperative to ensure the security of CPSs. Given that many CPSs provide critical services, for instance many critical infrastructure (CI) are CPSs such as smart girds and nuclear reactors; it is then inevitable to ensure that these critical CPSs can maintain proper operation. One key measure of the CPS’s functionality, is resilience which evaluates the ability of a CPS to deliver its designated service under potentially disruptive situations. In general, resilience measures a CPS’s ability to adapt or rapidly recover from disruptive events. Therefore, it is crucial for CPSs to be resilient in face of potential failures. To this end, the central goal of this dissertation is to develop novel analytical frameworks that can evaluate and improve security and resilience of CPSs. In these frameworks, cross-disciplinary tools are used from game theory, contract theory, and optimization to develop robust analytical solutions for security and resilience problems. In particular, these frameworks led to the following key contributions in cyber security: developing an analytical framework to mitigate the effects of GPS spoofing attacks against UAVs, introducing a game-theoretic moving target defense (MTD) framework to improve the cyber security, and securing data privacy in m-health Internet of Things (IoT) networks using a MTD cryptographic framework. In addition, the dissertation led to the following contributions in CI resilience: developing a general framework using Bayesian Networks to evaluate and improve the resilience of CIs against their components failure, introducing a contract-theoretic model to allocate resources to multiple connected CIs under complete and asymmetric information scenarios, providing a proactive plan to improve the resilience of transportation networks against flooding, and, finally, developing an environment-aware framework to deploy UAVs in disaster-areas.

Cyber-Physical Systems (CPSes) form the core of Industrial Control Systems (ICS) and critical infrastructures. These systems use computers to control and monitor physical processes in many critical industries including aviation, industrial automation, transportation, communications, waste treatment, and power systems. Increasingly, these systems are connected with corporate networks and the Internet, making them susceptible to risks similar to traditional computing systems experiencing cyber-attacks on a conventional IT network. Furthermore, recent attacks like the Stuxnet worm have demonstrated the weaknesses of CPS security, which has gained much attention in the research community to develop more effective security mechanisms. While this remains an important topic of research, often CPS security is not given much attention in undergraduate programs. There can be a significant disconnect between control system engineers with CPS engineering skills and network engineers with an IT background. This thesis describes hands-on courseware to help students bridge this gap. This courseware incorporates cyber-physical security concepts into effective learning modules that highlight real-world technical issues. A modular learning approach helps students understand CPS architectures and their vulnerabilities to cyber-attacks via experiential learning, and acquire practical skills through actively participating in the hands-on exercises. The ultimate goal of these lab modules is to show how an adversary would break into a conventional CPS system by exploiting various network protocols and security measures implemented in the system. A mock testbed environment is created using commercial-off-the-shelf hardware to address the unique aspects of a CPS, and serve as a cybersecurity trainer for students from control system or IT backgrounds. The modular nature of this courseware, which uses an economical and easily replicable hardware testbed, make this experience uniquely available as an adjunct to a conventional embedded system, control system design, or cybersecurity courses. To assess the impact of this courseware, an evaluation survey is developed to measure the understanding of the unique aspects of CPS security addressed. These modules leverage the existing academic subjects, help students understand the sequence of steps taken by adversaries, and serve to bridge theory and practice. / Master of Science

The field of robotics has witnessed significant advancements in recent years, with robotic arms playing a pivotal role in various industrial and research applications. In large-scale manufacturing, manual labour has been replaced with robots due to their efficiency in time and cost. However, in order to replace human labour, the robots need to collaborate in a way that humans do. This master's thesis, conducted at the Cyber-physical Systems Lab (CPS-Lab) at Uppsala University, delves into the intricacies of cooperative interactions between two homogenous robotic arms powered by machine learning algorithms, aiming to explore their collective capabilities. The project will focus on implementing a multi-agent cart-pole experiment that will challenge the two robotic arms' cooperative abilities. First, the problem is simulated, and afterwards implemented in real life. The experiment will be evaluated by the performance of various tested machine learning algorithms. In the end, The simulation yielded poor results due to the complexity of the problem and the lack of proper hyperparameter tuning. The real life experiment failed instantly, caused by the robotic arms not being designed for this application, a large simulation gap, and latency in the controller design. Overall, the results show that the experiment was challenging for the robotic arms, but that it might be possible under different circumstances.

Remote control of automation systems is consistently undeniably as a crucial aspect of their development, as it eliminates the need to travel unnecessary distances to operate them. Therefore, a framework is proposed not only for controlling an industrial robotic system but also for monitoring its behaviour and environment to ensure efficient and secure control over it. This project is carried out within the field of robotics, although its application can extend to other domains such as automotive, among others.  In the following project, a system based on industry 5.0 and Cyber Physical Systems is developed and implemented capable of storing and recovering the data collected from a robotic station while allowing its control through a User Interface. Giving the operator the opportunity to control an industrial assembly process remotely in a reliable and safe way.

This publication demonstrates that determining the maximum speed or Ideal Cycle Time (ICT) of machinery or cyber-physical systems is crucial for uncovering the limits of automation in a given system. Possibly increasing Overall Equipment Effectiveness (OEE) and identify opportunities for further digitization, automation, and AI integration. Based on literature review and expert interviews, methods for establishing ICT, mentioned in literature were identified and crosscheck with what practitioners in operation actually use and how they apply them. The identified methods were: Empirical Measurement and Data Analysis, Time Studies, Statistical Process Control (SPC), Benchmarking, Simulation and Modeling, Expert Judgment, and Continuous Improvement Practices we. We contrast these with insights obtained from interviews conducted with experts from companies in the German federal State of Brandenburg, representing diverse industries and sectors. Findings suggest that while companies recognize somewhat their ICT or maximum operational speeds, they often lack a structured method for determining them. They frequently use combinations of established methods inconsistently. We deduce that a formalized approach to defining ICT can better reveal system limitations and potential for expanding them through advanced automation and. We argue that a well-defined ICT is essential for pushing the boundaries of automated systems, contributing to more effective and Humanity–Centered Automation (HCA) solutions.

Doctor of Philosophy / Computing and Information Sciences / Scott A. DeLoach / As sensor prices drop and computing devices continue to become more compact and powerful, computing capabilities are being embedded throughout our physical environment. Connecting these devices in cyber-physical systems (CPS) enables applications with significant societal impact and economic benefit. However, engineering CPS poses modeling, architecture, and engineering challenges and, to fully realize the desired benefits, many outstanding challenges must be addressed. For the cyber parts of CPS, two decades of work in the design of autonomous agents and multiagent systems (MAS) offers design principles for distributed intelligent systems and formalizations for agent-oriented software engineering (AOSE). MAS foundations offer a natural fit for enabling distributed interacting devices. In some cases, complex control structures such as holarchies can be advantageous. These can motivate complex organizational strategies when implementing such systems with a MAS, and some designs may require agents to act in multiple groups simultaneously. Such agents must be able to manage their multiple associations and assignments in a consistent and unambiguous way. This thesis shows how designing agents as systems of intelligent subagents offers a reusable and practical approach to designing complex systems. It presents a set of flexible, reusable components developed for OBAA++, an organization-based architecture for single-group MAS, and shows how these components were used to develop the Adaptive Architecture for Systems of Intelligent Systems (AASIS) to enable multigroup agents suitable for complex, multigroup MAS. This work illustrates the reusability and flexibility of the approach by using AASIS to simulate a CPS for an intelligent power distribution system (IPDS) operating two multigroup MAS concurrently: one providing continuous voltage control and a second conducting discrete power auctions near sources of distributed generation.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Don Gruenbacher / Caterina Scoglio / Cyber physical systems emerge when physical systems are integrated with communication networks. In particular, communication networks facilitate dissemination of data among components of physical systems to meet key requirements, such as efficiency and reliability, in achieving an objective. In this dissertation, we consider one of the most important cyber physical systems: the smart grid. The North American Electric Reliability Corporation (NERC) envisions a smart grid that aggressively explores advance communication network solutions to facilitate real-time monitoring and dynamic control of the bulk electric power system. At the distribution level, the smart grid integrates renewable generation and energy storage mechanisms to improve reliability of the grid. Furthermore, dynamic pricing and demand management provide customers an avenue to interact with the power system to determine electricity usage that satisfies their lifestyle. At the transmission level, efficient communication and a highly automated architecture provide visibility in the power system; hence, faults are mitigated faster than they can propagate. However, higher levels of reliability and efficiency rely on the supporting physical communication infrastructure and the network technologies employed. Conventionally, the topology of the communication network tends to be identical to that of the power network. In this dissertation, however, we employ a Demand Response (DR) application to illustrate that a topology that may be ideal for the power network may not necessarily be ideal for the communication network. To develop this illustration, we realize that communication network issues, such as congestion, are addressed by protocols, middle-ware, and software mechanisms. Additionally, a network whose physical topology is designed to avoid congestion realizes an even higher level of performance. For this reason, characterizing the communication infrastructure of smart grids provides mechanisms to improve performance while minimizing cost. Most recently, algebraic connectivity has been used in the ongoing research effort characterizing the robustness of networks to failures and attacks. Therefore, we first derive analytical methods for increasing algebraic connectivity and validate these methods numerically. Secondly, we investigate impact on the topology and traffic characteristics as algebraic connectivity is increased. Finally, we construct a DR application to demonstrate how concepts from graph theory can dramatically improve the performance of a communication network. With a hybrid simulation of both power and communication network, we illustrate that a topology which may be ideal for the power network may not necessarily be ideal for the communication network. To date, utility companies are embracing network technologies such as Multiprotocol Label Switching (MPLS) because of the available support for legacy devices, traffic engineering, and virtual private networks (VPNs) which are essential to the functioning of the smart grid. Furthermore, this particular network technology meets the requirement of non-routability as stipulated by NERC, but these benefits are costly for the infrastructure that supports the full MPLS specification. More importantly, with MPLS routing and other switching technologies, innovation is restricted to the features provided by the equipment. In particular, no practical method exists for utility consultants or researchers to test new ideas, such as alternatives to IP or MPLS, on a realistic scale in order to obtain the experience and confidence necessary for real-world deployments. As a result, novel ideas remain untested. On the contrary, OpenFlow, which has gained support from network providers such as Microsoft and Google and equipment vendors such as NEC and Cisco, provides the programmability and flexibility necessary to enable innovation in next-generation communication architectures for the smart grid. This level of flexibility allows OpenFlow to provide all features of MPLS and allows OpenFlow devices to co-exist with existing MPLS devices. Therefore, in this dissertation we explore a low-cost OpenFlow Software Defined Networking solution and compare its performance to that of MPLS. In summary, we develop methods for designing robust networks and evaluate software defined networking for communication and control in cyber physical systems where the smart grid is the system under consideration.

La gestion des suites chirurgicales joue un rôle central pour permettre aux hôpitaux d’offrir l’accès aux soins à des coûts raisonnables. L'informatisation et l'automatisation sont des évolutions conventionnelles pour améliorer l’efficacité. Toutefois, un soutien inadapté ne peut améliorer l'activité de gestion et peut nuire à son action. Notre hypothèse est que des fonctionnalités interactives, utilisables, flexibles et adaptée aux spécificités des activités locales peuvent créer un environnement de travail dans lequel le personnel médical est capable de réagir à des événements inattendus et de s’approprier la technologie. Nos contributions comprennent en une analyse de l'activité de l'équipe chirurgicale, basée sur des entretiens, observations, une revue de la littérature et une analogie avec l'aviation civile. Nous avons participé à la construction d'un modèle mathématique du flux chirurgical et d'une visualisation de ce modèle. Nous avons identifié les exigences et principes de conception nécessaires au développement, à l'intégration et à l'appropriation d'un outil pour soutenir la gestion du flux chirurgical. Nous avons conçu des interactions multi-utilisateurs sur une grande surface et développé un prototype de tableau blanc électronique, OnBoard, qui démontre l'intégration des spécifications et des défis techniques. OnBoard appartient à un système cyber-physique comprenant des capteurs dans les salles d'opération. Enfin, nous avons déployé et évalué OnBoard dans une suite chirurgicale. L'expérience de OnBoard suggère que la conception des interactions est primordiale pour offrir un environnement collaboratif efficace au personnel médical. / Surgical suite management plays a key role in the endeavor of hospitals: patients’ health at sustainable cost. Computerization and automation of processes are conventional solutions to support resource management and efficiency. However, unsuitable support might not improve the management activity, and can even be detrimental to it. Our hypothesis is that usable and flexible interactivity tuned to local particularities can create a working environment in which the medical staff can cope with unexpected surgery events and appropriate the technology. Our contributions comprise an analysis of the activity of the surgical team, based on interviews, observations, review of the literature and an analogy with civil aviation. We participated in the construction of a mathematical model of the surgical workflow and a visualization of the mathematical model. We conducted an experimentation to identify bottlenecks of workflow inefficiencies and delays. We identified scenarios, requirements and design principles necessary to the development, integration and acceptation of a tool to support surgical workflow activities. We designed multi-users interactions on a large surface and made a prototype of electronic whiteboard, OnBoard, for the surgical suite which demonstrates the integration of the specifications and technical challenges. OnBoard belongs to a larger cyber physical system including activity sensors in every operating room of the surgical suite. Finally, we deployed the prototype in a surgical suite and evaluated it. The OnBoard experience suggests that the design of interactions is paramount to provide the medical staff an efficient collaborative environment.

<div>This dissertation addresses two significant issues in the effective application of machine learning algorithms and models for the physical and engineering sciences. The first is the broad challenge of automated modeling of data across different processes in a physical system. The second is the dilemma of obtaining insightful interpretations on the relationships between the inputs and outcome of a system as inferred from complex, black box machine learning models.</div><div><br></div><div><b>Automated Geometric Shape Deviation Modeling for Additive Manufacturing Systems</b></div><div><b><br></b></div><div>Additive manufacturing systems possess an intrinsic capability for one-of-a-kind manufacturing of a vast variety of shapes across a wide spectrum of processes. One major issue in AM systems is geometric accuracy control for the inevitable shape deviations that arise in AM processes. Current effective approaches for shape deviation control in AM involve the specification of statistical or machine learning deviation models for additively manufactured products. However, this task is challenging due to the constraints on the number of test shapes that can be manufactured in practice, and limitations on user efforts that can be devoted for learning deviation models across different shape classes and processes in an AM system. We develop an automated, Bayesian neural network methodology for comprehensive shape deviation modeling in an AM system. A fundamental innovation in this machine learning method is our new and connectable neural network structures that facilitate the transfer of prior knowledge and models on deviations across different shape classes and AM processes. Several case studies on in-plane and out-of-plane deviations, regular and free-form shapes, and different settings of lurking variables serve to validate the power and broad scope of our methodology, and its potential to advance high-quality manufacturing in an AM system.</div><div><br></div><div><b>Interpretable Machine Learning</b></div><div><b><br></b></div><div>Machine learning algorithms and models constitute the dominant set of predictive methods for a wide range of complex, real-world processes. However, interpreting what such methods effectively infer from data is difficult in general. This is because their typical black box natures possess a limited ability to directly yield insights on the underlying relationships between inputs and the outcome for a process. We develop methodologies based on new predictive comparison estimands that effectively enable one to ``mine’’ machine learning models, in the sense of (a) interpreting their inferred associations between inputs and/or functional forms of inputs with the outcome, (b) identifying the inputs that they effectively consider relevant, and (c) interpreting the inferred conditional and two-way associations of the inputs with the outcome. We establish Fisher consistent estimators, and their corresponding standard errors, for our new estimands under a condition on the inputs' distributions. The significance of our predictive comparison methodology is demonstrated with a wide range of simulation and case studies that involve Bayesian additive regression trees, neural networks, and support vector machines. Our extended study of interpretable machine learning for AM systems demonstrates how our method can contribute to smarter advanced manufacturing systems, especially as current machine learning methods for AM are lacking in their ability to yield meaningful engineering knowledge on AM processes. <br></div>