Cyber-physical systems (CPS) need to be designed to deal with various forms of uncertainty associated with data contributing to the system's knowledge of the environment. Dealing with uncertainty requires adopting an appropriate model, which then allows making the right decisions and carrying out the right actions (possibly affecting the environment) based on imperfect information. However, choosing and incorporating a suitable model into CPS design is difficult, because it requires identifying the kind of uncertainty at hand as well as knowledge of suitable models and their application to dealing with the uncertainty. While inspiration can be found in other CPS designs, the details of dealing with uncertainty in another CPS can be confounded by domain-specific terminology, context, and requirements. To make this aspect of CPS design less daunting, we aim at providing an overview of approaches dealing with uncertainty in the design of CPS targeting collective behavior. To this end, we present a systematic review of relevant scientific projects with industrial leadership and synthesis of relations between system features, the kinds of uncertainty, and methods used to deal with it. The results provide an overview of uncertainty across different domains and challenges and reason about a guide for...

<div>Cyber-physical systems, through various sensors and actuators, are used to handle interactions of the cyber-world with the physical-world. Conventionally, the temporal component of the physical-world has been used only for estimating real-time deadlines and responsiveness of control-loop algorithms. However, there are various other applications where the relationship of the temporal component and the cyber-world are of interest. An example is the ability to reconstruct a sequence of past temporal activities from the current state of the cyber-world, which is of obvious interest to cyber-forensic investigators. Another example is the ability to control the temporal components in broadcast communication networks, which leads to new attack and defense capabilities. These relationships have not been explored traditionally.</div><div><br></div><div>To address this gap, this dissertation proposes three systems that cast light on the effect of temporal component of the physical-world on the cyber-world. First, we present Timeliner, a smartphone cyber-forensics technique that recovers past actions from a single static memory image. Following that, we present work on CAN (Controller Area Network), a broadcast communication network used in automotive applications. We show in DUET that the ability to control communication temporally allows two compromised ECUs, an attacker and an accomplice, to stealthily suppress and impersonate a victim ECU, even in the presence of a voltage-based intrusion detection system. In CANDID, we show that the ability to temporally control CAN communication opens up new defensive capabilities that make the CAN much more secure.</div><div><br></div><div>The evaluation results show that Timeliner is very accurate and can reveal past evidence (up to an hour) of user actions across various applications on Android devices. The results also show that DUET is highly effective at impersonating victim ECUs while evading both message-based and voltage-based intrusion detection systems, irrespective of the features and the training algorithms used. Finally, CANDID is able to provide new defensive capabilities to CAN environments with reasonable communication and computational overheads.</div><div><br></div>

Cyber-physical systems, built from the integration of cyber and physical components, are being used in multiple domains ranging from manufacturing and healthcare to traffic con- trol and safety. Ensuring the security of cyber-physical systems is crucial because they provide the foundation of the critical infrastructure, and security incidents can result in catastrophic failures. Recent publications report that machine learning models are vul- nerable to adversarial examples, crafted by adding small perturbations to input data. For the past decade, machine learning security has become a growing interest area, with a significant number of systematic reviews and surveys that have been published. Secu- rity of artificial intelligence in cyber-physical systems is more challenging in comparison to machine learning security, because adversaries have a wider possible attack surface, in both cyber and physical domains. However, comprehensive systematic literature re- views in this research field are not available. Therefore, this work presents a systematic literature review of the adversarial attacks on artificial intelligence in cyber-physical sys- tems, examining 45 scientific papers, selected from 134 publications found in the Scopus database. It provides the classification of attack algorithms and defense methods, the sur- vey of evaluation metrics, an overview of the state of the art in methodologies and tools, and, as the main contribution, identifies open problems and research gaps and highlights future research challenges in this area of interest.

Complex cyber-physical systems have become fundamental to modern society by effectively providing critical services and improving efficiency in various domains. Unfortunately, as systems become more connected and more complex, they also can become more vulnerable and less robust. As a result, various failure modes become more common and easily triggered from both unanticipated and malicious perturbations. Research has been conducted in the area of vulnerability analysis for cyber-physical systems, to assist in locating these possible vulnerabilities before they can fail. I present two case studies on different forms of critical infrastructure systems to identify vulnerabilities and understand how external perturbations can affect them, namely UAV drone swarms and municipal water infrastructure.

Cyberattacks against critical infrastructures has been a growing problem for the past couple of years. These infrastructures are a particularly desirable target for adversaries, due to their vital importance in society. For instance, a stop in the operation of a critical infrastructure could result in a crippling effect on a nation's economy, security or public health. The reason behind this increase is that critical infrastructures have become more complex, often being integrated with a large network of various cyber components. It is through these cyber components that an adversary is able to access the system and conduct their attacks. In this thesis, we consider methods which can be used as a first line of defence against such attacks for Cyber-Physical Systems (CPS). Specifically, we start by studying how information leaks about a system's dynamics helps an adversary to generate attacks that are difficult to detect. In many cases, such attacks can be detrimental to a CPS since they can drive the system to a breaking point without being detected by the operator that is tasked to secure the system. We show that an adversary can use small amounts of data procured from information leaks to generate these undetectable attacks. In particular, we provide the minimal amount of information that is needed in order to keep the attack hidden even if the operator tries to probe the system for attacks.  We design defence mechanisms against such information leaks using the Hammersley-Chapman-Robbins lower bound. With it, we study how information leakage could be mitigated through corruption of the data by injection of measurement noise. Specifically, we investigate how information about structured input sequences, which we call events, can be obtained through the output of a dynamical system and how this leakage depends on the system dynamics. For example, it is shown that a system with fast dynamical modes tends to disclose more information about an event compared to a system with slower modes. However, a slower system leaks information over a longer time horizon, which means that an adversary who starts to collect information long after the event has occured might still be able to estimate it. Additionally, we show how sensor placements can affect the information leak. These results are then used to aid the operator to detect privacy vulnerabilities in the design of a CPS. Based on the Hammersley-Chapman-Robbins lower bound, we provide additional defensive mechanisms that can be deployed by an operator online to minimize information leakage. For instance, we propose a method to modify the structured inputs in order to maximize the usage of the existing noise in the system. This mechanism allows us to explicitly deal with the privacy-utility trade-off, which is of interest when optimal control problems are considered. Finally, we show how the adversary's certainty of the event increases as a function of the number of samples they collect. For instance, we provide sufficient conditions for when their estimation variance starts to converge to its final value. This information can be used by an operator to estimate when possible attacks from an adversary could occur, and change the CPS before that, rendering the adversary's collected information useless. / De senaste åren har cyberanfall mot kritiska infrastructurer varit ett växande problem. Dessa infrastrukturer är särskilt utsatta för cyberanfall, eftersom de uppfyller en nödvändig function för att ett samhälle ska fungera. Detta gör dem till önskvärda mål för en anfallare. Om en kritisk infrastruktur stoppas från att uppfylla sin funktion, då kan det medföra förödande konsekvenser för exempelvis en nations ekonomi, säkerhet eller folkhälsa. Anledningen till att mängden av attacker har ökat beror på att kritiska infrastrukturer har blivit alltmer komplexa eftersom de numera ingår i stora nätverk dör olika typer av cyberkomponenter ingår. Det är just genom dessa cyberkomponenter som en anfallare kan få tillgång till systemet och iscensätta cyberanfall. I denna avhandling utvecklar vi metoder som kan användas som en första försvarslinje mot cyberanfall på cyberfysiska system (CPS). Vi med att undersöka hur informationsläckor om systemdynamiken kan hjälpa en anfallare att skapa svårupptäckta attacker. Oftast är sådana attacker förödande för CPS, eftersom en anfallare kan tvinga systemet till en bristningsgräns utan att bli upptäcka av operatör vars uppgift är att säkerställa systemets fortsatta funktion. Vi bevisar att en anfallare kan använda relativt små mängder av data för att generera dessa svårupptäckta attacker. Mer specifikt så härleder ett uttryck för den minsta mängd information som krävs för att ett anfall ska vara svårupptäckt, även för fall då en operatör tar till sig metoder för att undersöka om systemet är under attack. I avhandlingen konstruerar vi försvarsmetoder mot informationsläcker genom Hammersley-Chapman-Robbins olikhet. Med denna olikhet kan vi studera hur informationsläckan kan dämpas genom att injicera brus i datan. Specifikt så undersöker vi hur mycket information om strukturerade insignaler, vilket vi kallar för händelser, till ett dynamiskt system som en anfallare kan extrahera utifrån dess utsignaler. Dessutom kollar vi på hur denna informationsmängd beror på systemdynamiken. Exempelvis så visar vi att ett system med snabb dynamik läcker mer information jämfört med ett långsammare system. Däremot smetas informationen ut över ett längre tidsintervall för långsammare system, vilket leder till att anfallare som börjar tjuvlyssna på ett system långt efter att händelsen har skett kan fortfarande uppskatta den. Dessutom så visar vi jur sensorplaceringen i ett CPS påverkar infromationsläckan. Dessa reultat kan användas för att bistå en operatör att analysera sekretessen i ett CPS. Vi använder även Hammersley-Chapman-Robbins olikhet för att utveckla försvarslösningar mot informationsläckor som kan användas \textit{online}. Vi föreslår modifieringar till den strukturella insignalen så att systemets befintliga brus utnyttjas bättre för att gömma händelsen. Om operatören har andra mål den försöker uppfylla med styrningen så kan denna metod användas för att styra avvängingen mellan sekretess och operatorns andra mål. Slutligen så visar vi hur en anfallares uppskattning av händelsen förbättras som en funktion av mängden data får tag på. Operatorn kan använda informationen för att ta reda på när anfallaren kan tänka sig vara redo att anfalla systemet, och därefter ändra systemet innan detta sker, vilket gör att anfallarens information inte längre är användbar. / <p>QC 20210820</p>

Esta investigación doctoral aborda la robótica asistencial (Assistive Robotics) como tema general, también llamada robótica social; específicamente el estudio de la interacción de la relación humano-robot. Con base en el estudio del estado del arte realizado, esta tesis se orientó hacia el siguiente problema de investigación: ¿es posible mejorar la efectividad terapéutica y de esta forma la calidad de vida de las personas con dificultades para comunicarse verbalmente debido a singularidades relacionadas con el habla y lenguaje; por medio del desarrollo de estrategias socio-terapéuticas que utilicen robots y mundos virtuales con frecuencias de exposición flexibles en comparación con la exposición programada utilizando únicamente robots en el mundo físico? El problema de investigación brinda una direccionalidad innovadora desde varias perspectivas científicas. Por un lado, la integración terapéutica de recursos en el mundo virtual así como en el mundo físico con robótica asistencial en colaboración con el ser humano para cumplir un objetivo de crecimiento personal. Además, la posibilidad de romper la barrera del tiempo controlado de exposición terapéutica por medio de la tecnología. Y, por otro lado, metodológicamente buscar un abordaje científico que demuestre causalidad y no sólo asociación por medios cualitativos; dado que se quiere saber si la efectividad terapéutica realmente puede incrementar como variable de respuesta. Por lo tanto, y como elemento de innovación adicional, esta investigación abordó el diseño de un prototipo tecnológico de programación robótica con emulación animada, que integra el uso de un avatar robótico virtual para facilitar la interacción social de personas que presentan dificultades de comunicación verbal relacionadas con el habla y lenguaje. El objetivo general de esta tesis se plantea como sigue: “Aportar al conocimiento científico sobre la interacción humano-robot con fines terapéuticos de comunicación verbal en el idioma castellano comparando el uso de robots en el mundo físico y virtual con flexibilidad de tiempos versus la interacción limitada a robots físicos por periodos de tiempo fijos para saber si es posible incrementar de forma relevante la efectividad terapéutica en términos de mejora de habilidades y tiempo invertido en terapia.” Esta investigación contribuye científicamente con la propuesta de un enfoque metodológico que busca obtener resultados basados en la evidencia experimental y no sólo en el análisis hermenéutico o el análisis léxico de datos cualitativos que constituye lo más frecuente en la investigación científica en este campo. En este sentido, el método exploratorio basado en datos cualitativos y abordajes epistemológicos subjetivistas pueden verse bien complementados con investigación positivista más orientada a la evidencia basada en resultados vinculados a la causalidad. Otra aportación de esta investigación está en el desarrollo tecnológico orientado hacia el uso de la experiencia de realidad virtual de personas con condiciones de limitación en habla o lenguaje en combinación con un robot físico. Esta es una forma innovadora de buscar la exposición continua y en tiempo real a los protocolos de terapia de habla sin supervisión física del terapeuta, teniendo en cuenta que las aplicaciones robóticas en mundos virtuales vinculados con el "mundo físico" no son frecuentes.

This thesis explores how a detection engine using Artificial Neural Networks (ANNs) could be implemented within the DETECT framework. The framework is used for security purposes in Cyber-physical systems. These are critical systems often vital to important infrastructure so discovering new ways of how to defend against threats is of huge importance. However, there are many difficult challenges that needs to be addressed before employing an ANN as a threat detection mechanism. Most notable what kind of ANN to use, training data and issues such as over-fitting. These challenges were addressed in the model that was created for this paper. The model was based on the current literature and previous research. To make informed decisions about the model a literature review was carried out to gather as much information as possible. A key insight from the review was the use of recurrent neural networks for threat detection.

This thesis investigates the possibility of connecting an embedded device, STM32 B-L4S5I-IOT01A IoT discovery node, to a Eclipse Arrowhead framework local cloud.This thesis also examines the benefits of using the Eclipse Arrowhead framework compared to its competitors Amazon Web Services and Microsoft Azure. The world is entering a new industrial revolution, often referred to as Industry 4.0, moving towards a more decentralized and software-oriented means of production.This fourth industrial revolution incorporates System of Systems, Cyber-Physical Systems, and embedded software technologies. One of the internet-based industrial solutions is the Eclipse Arrowhead framework. The Eclipse Arrowhead framework contains many examples in various promgramming languages and technologies but lacks an example of a specific piece of hardware connecting to a local Eclipse Arrowhead cloud.Therefore, a project with the clear intent to showcase both the capabilities and possibilities of Cyber-Physical systems and the Eclipse Arrowhead framework is needed. The system this thesis implements consists of three major parts: the stm32 board, a Python flask app, and the Eclipse Arrowhead framework.The main objective of the Eclipse Arrowhead framework is to connect the consumer and the provider in a safe and structured way.The provider is built with C/C++ using ARMs' mbed os.  The response time of the different frameworks, Eclipse Arrowhead framework and Amazon Web Services, was measured.We made a thousand attempts to form an adequate basis for an average response time. In addition to presenting the average response time, we calculated the maximum and minimum response times to understand the different frameworks' performance further.  The thesis shows some benefits in response time when running an Eclipse Arrowhead framework local cloud instead of using a remote service such as Amazon Web Services. Average response time decreased by 17.5 times while running an Eclipse Arrowhead framework local cloud.Maximum and minimum response times decreased by 1.9 and 134 times, respectively.

Smart manufacturing promotes the demand of a new interface for communication with virtual entities such as big data analysis model, digital twin, and autonomous software programs. Although ideal smart manufacturing pursues full automation by self-adoption and self-decision of autonomy, converging human intervention and collaboration with the autonomy have shown significant improvements on productivity and quality, and it expects more advancements on current manufacturing trend. This study aims how to combine human and autonomy based on current technical advancement of smart manufacturing. In detail, creating networks between the entities, developing a new interface for human-autonomy collaboration, and demonstrating the effectiveness of the collaboration are main research topics.<br>

<p>Establishing permanent and sustainable human settlements outside Earth presents numerous challenges. The Resilient Extra-Terrestrial Habitat Institute (RETHi) has been established to advance the fundamental knowledge needed to enable and design resilient habitats in deep space, that will adapt, absorb, and rapidly recover from expected and unexpected disruptions without fundamental changes in function or sacrifices in safety.</p> <p>Future extra-terrestrial habitats will rely on several subsystems working synergistically to ensure adequate power supply, life support to crew members, manage extreme environmental conditions, and monitor the health status of the equipment. To study extra-terrestrial habitats, a combination of modeling approaches and experimental validations is necessary, but deep-space conditions cannot be entirely reproduced in a laboratory setting (e.g., micro-gravity effects). To this end, real-time multi-physics cyber-physical testing is a novel approach of simulating and evaluating complex system-of-systems (SoS) that has been applied to investigate the behavior of extra-terrestrial habitats under different scenarios (e.g., meteorite strikes). One of the most critical components which determines the success of the cyber-physical testbed is the transfer system serving as an interface between the physical and cyber substructures.</p> <p>Through this work, a dedicated thermal transfer system has been designed and constructed to provide realistic thermal boundary conditions to the physical habitat according to the real-time simulation results from cyber substructure of the habitat. The extreme temperatures to be found at the interface between the external protective layer of the habitat (cyber) and the interior structural elements (physical) are emulated by means of a cryogenic chiller and an array of cooled panels that cover a dome-style structure. Moreover, the overall architecture of the cyber-physical testbed, the partitioning of the virtual and physical environments, and interface schemes were also established. The experimental results obtained from the thermal transfer system prototype setup were analyzed and interpreted to generate meaningful recommendations for future development and application of the full-sized testbed.</p>