Autonomous cyber-physical systems (CPSs) such as autonomous connected vehicles (ACVs), unmanned aerial vehicles (UAVs), critical infrastructure (CI), and the Internet of Things (IoT) will be essential to the functioning of our modern economies and societies. Therefore, maintaining the autonomy of CPSs as well as their stability, robustness, and security (SRS) in face of exogenous and disruptive events is a critical challenge. In particular, it is crucial for CPSs to be able to not only operate optimally in the vicinity of a normal state but to also be robust and secure so as to withstand potential failures, malfunctions, and intentional attacks. However, to evaluate and improve the SRS of CPSs one must overcome many technical challenges such as the unpredictable behavior of a CPS's cyber-physical environment, the vulnerability to various disruptive events, and the interdependency between CPSs. The primary goal of this dissertation is, thus, to develop novel foundational analytical tools, that weave together notions from machine learning, game theory, and control theory, in order to study, analyze, and optimize SRS of autonomous CPSs. Towards achieving this overarching goal, this dissertation led to several major contributions. First, a comprehensive control and learning framework was proposed to thwart cyber and physical attacks on ACV networks. This framework brings together new ideas from optimal control and reinforcement learning (RL) to derive a new optimal safe controller for ACVs in order to maximize the street traffic flow while minimizing the risk of accidents. Simulation results show that the proposed optimal safe controller outperforms the current state of the art controllers by maximizing the robustness of ACVs to physical attacks. Furthermore, using techniques from convex optimization and deep RL a joint trajectory and scheduling policy is proposed in UAV-assisted networks that aims at maintaining the freshness of ground node data at the UAV. The analytical and simulation results show that the proposed policy can outperform policies such discretized state RL and value-based methods in terms of maximizing the freshness of data. Second, in the IoT domain, a novel watermarking algorithm, based on long short term memory cells, is proposed for dynamic authentication of IoT signals. The proposed watermarking algorithm is coupled with a game-theoretic framework so as to enable efficient authentication in massive IoT systems. Simulation results show that using our approach, IoT messages can be transmitted from IoT devices with an almost 100% reliability. Next, a brainstorming generative adversarial network (BGAN) framework is proposed. It is shown that this framework can learn to generate real-looking data in a distributed fashion while preserving the privacy of agents (e.g. IoT devices, ACVs, etc). The analytical and simulation results show that the proposed BGAN architecture allows heterogeneous neural network designs for agents, works without reliance on a central controller, and has a lower communication over head compared to other state-of-the-art distributed architectures. Last, but not least, the SRS challenges of interdependent CI (ICI) are addressed. Novel game-theoretic frameworks are proposed that allow the ICI administrator to assign different protection levels on ICI components to maximizing the expected ICI security. The mixed-strategy Nash of the games are derived analytically. Simulation results coupled with theoretical analysis show that, using the proposed games, the administrator can maximize the security level in ICI components. In summary, this dissertation provided major contributions across the areas of CPSs, machine learning, game theory, and control theory with the goal of ensuring SRS across various domains such as autonomous vehicle networks, IoT systems, and ICIs. The proposed approaches provide the necessary fundamentals that can lay the foundations of SRS in CPSs and pave the way toward the practical deployment of autonomous CPSs and applications. / Doctor of Philosophy / In order to deliver innovative technological services to their residents, smart cities will rely on autonomous cyber-physical systems (CPSs) such as cars, drones, sensors, power grids, and other networks of digital devices. Maintaining stability, robustness, and security (SRS) of those smart city CPSs is essential for the functioning of our modern economies and societies. SRS can be defined as the ability of a CPS, such as an autonomous vehicular system, to operate without disruption in its quality of service. In order to guarantee SRS of CPSs one must overcome many technical challenges such as CPSs' vulnerability to various disruptive events such as natural disasters or cyber attacks, limited resources, scale, and interdependency. Such challenges must be considered for CPSs in order to design vehicles that are controlled autonomously and whose motion is robust against unpredictable events in their trajectory, to implement stable Internet of digital devices that work with a minimum communication delay, or to secure critical infrastructure to provide services such as electricity, gas, and water systems. The primary goal of this dissertation is, thus, to develop novel foundational analytical tools, that weave together notions from machine learning, game theory, and control theory, in order to study, analyze, and optimize SRS of autonomous CPSs which eventually will improve the quality of service provided by smart cities. To this end, various frameworks and effective algorithms are proposed in order to enhance the SRS of CPSs and pave the way toward the practical deployment of autonomous CPSs and applications. The results show that the developed solutions can enable a CPS to operate efficiently while maintaining its SRS. As such, the outcomes of this research can be used as a building block for the large deployment of smart city technologies that can be of immense benefit to tomorrow's societies.

Cyber-physical systems (CPSs) are large-scale systems that seamlessly integrate physical and human elements via a cyber layer that enables connectivity, sensing, and data processing. Key examples of CPSs include smart power systems, smart transportation systems, and the Internet of Things (IoT). This wide-scale cyber-physical interconnection introduces various operational benefits and promises to transform cities, infrastructure, and networked systems into more efficient, interactive, and interconnected smart systems. However, this ubiquitous connectivity leaves CPSs vulnerable to menacing security threats as evidenced by the recent discovery of the Stuxnet worm and the Mirai malware, as well as the latest reported security breaches in a number of CPS application domains such as the power grid and the IoT. Addressing these culminating security challenges requires a holistic analysis of CPS security which necessitates: 1) Determining the effects of possible attacks on a CPS and the effectiveness of any implemented defense mechanism, 2) Analyzing the multi-agent interactions -- among humans and automated systems -- that occur within CPSs and which have direct effects on the security state of the system, and 3) Recognizing the role that humans and their decision making processes play in the security of CPSs. Based on these three tenets, the central goal of this dissertation is to enhance the security of CPSs with human actors by developing fool-proof defense strategies founded on novel theoretical frameworks which integrate the engineering principles of CPSs with the mathematical concepts of game theory and human behavioral models. Towards realizing this overarching goal, this dissertation presents a number of key contributions targeting two prominent CPS application domains: the smart electric grid and drone systems. In smart grids, first, a novel analytical framework is developed which generalizes the analysis of a wide set of security attacks targeting the state estimator of the power grid, including observability and data injection attacks. This framework provides a unified basis for solving a broad set of known smart grid security problems. Indeed, the developed tools allow a precise characterization of optimal observability and data injection attack strategies which can target the grid as well as the derivation of optimal defense strategies to thwart these attacks. For instance, the results show that the proposed framework provides an effective and tractable approach for the identification of the sparsest stealthy attacks as well as the minimum sets of measurements to defend for protecting the system. Second, a novel game-theoretic framework is developed to derive optimal defense strategies to thwart stealthy data injection attacks on the smart grid, launched by multiple adversaries, while accounting for the limited resources of the adversaries and the system operator. The analytical results show the existence of a diminishing effect of aggregated multiple attacks which can be leveraged to successfully secure the system; a novel result which leads to more efficiently and effectively protecting the system. Third, a novel analytical framework is developed to enhance the resilience of the smart grid against blackout-inducing cyber attacks by leveraging distributed storage capacity to meet the grid's critical load during emergency events. In this respect, the results demonstrate that the potential subjectivity of storage units' owners plays a key role in shaping their energy storage and trading strategies. As such, financial incentives must be carefully designed, while accounting for this subjectivity, in order to provide effective incentives for storage owners to commit the needed portions of their storage capacity for possible emergency events. Next, the security of time-critical drone-based CPSs is studied. In this regard, a stochastic network interdiction game is developed which addresses pertinent security problems in two prominent time-critical drone systems: drone delivery and anti-drone systems. Using the developed network interdiction framework, the optimal path selection policies for evading attacks and minimizing mission completion times, as well as the optimal interdiction strategies for effectively intercepting the paths of the drones, are analytically characterized. Using advanced notions from Nobel-prize winning prospect theory, the developed framework characterizes the direct impacts of humans' bounded rationality on their chosen strategies and the achieved mission completion times. For instance, the results show that this bounded rationality can lead to mission completion times that significantly surpass the desired target times. Such deviations from the desired target times can lead to detrimental consequences primarily in drone delivery systems used for the carriage of emergency medical products. Finally, a generic security model for CPSs with human actors is proposed to study the diffusion of threats across the cyber and physical realms. This proposed framework can capture several application domains and allows a precise characterization of optimal defense strategies to protect the critical physical components of the system from threats emanating from the cyber layer. The developed framework accounts for the presence of attackers that can have varying skill levels. The results show that considering such differing skills leads to defense strategies which can better protect the system. In a nutshell, this dissertation presents new theoretical foundations for the security of large-scale CPSs, that tightly integrate cyber, physical, and human elements, thus paving the way towards the wide-scale adoption of CPSs in tomorrow's smart cities and critical infrastructure. / Ph. D. / Enhancing the efficiency, sustainability, and resilience of cities, infrastructure, and industrial systems is contingent on their transformation into more interactive and interconnected smart systems. This has led to the emergence of what is known as cyber-physical systems (CPSs). CPSs are widescale distributed and interconnected systems integrating physical components and humans via a cyber layer that enables sensing, connectivity, and data processing. Some of the most prominent examples of CPSs include the smart electric grid, smart cities, intelligent transportation systems, and the Internet of Things. The seamless interconnectivity between the various elements of a CPS introduces a wealth of operational benefits. However, this wide-scale interconnectivity and ubiquitous integration of cyber technologies render CPSs vulnerable to a range of security threats as manifested by recently reported security breaches in a number of CPS application domains. Addressing these culminating security challenges requires the development and implementation of fool-proof defense strategies grounded in solid theoretical foundations. To this end, the central goal of this dissertation is to enhance the security of CPSs by advancing novel analytical frameworks which tightly integrate the cyber, physical, and human elements of a CPS. The developed frameworks and tools enable the derivation of holistic defense strategies by: a) Characterizing the security interdependence between the various elements of a CPS, b) Quantifying the consequences of possible attacks on a CPS and the effectiveness of any implemented defense mechanism, c) Modeling the multi-agent interactions in CPSs, involving humans and automated systems, which have a direct effect on the security state of the system, and d) Capturing the role that human perceptions and decision making processes play in the security of CPSs. The developed tools and performed analyses integrate the engineering principles of CPSs with the mathematical concepts of game theory and human behavioral models and introduce key contributions to a number of CPS application domains such as the smart electric grid and drone systems. The introduced results enable strengthening the security of CPSs, thereby paving the way for their wide-scale adoption in smart cities and critical infrastructure.

No description available.

This thesis proposes a new method for detecting malicious cyber-physical attacks on additive manufacturing (AM) systems. The method makes use of a physical hash, which links digital data to the manufactured part via a disconnected side-channel measurement system. The disconnection ensures that if the network and/or AM system become compromised, the manufacturer can still rely on the measurement system for attack detection. The physical hash takes the form of a QR code that contains a hash string of the nominal process parameters and toolpath. It is manufactured alongside the original geometry for the measurement system to scan and compare to the readings from its sensor suite. By taking measurements in situ, the measurement system can detect in real-time if the part being manufactured matches the designer's specification. A proof-of-concept validation was realized on a material extrusion machine. The implementation was successful and demonstrated the ability of this method to detect the existence (and absence) of malicious attacks on both process parameters and the toolpath. A case study for detecting changes to the toolpath is also presented, which uses a simple measurement of how long each layer takes to build. Given benchmark readings from a 30x30 mm square layer created on a material extrusion system, several modifications were able to be detected. The machine's repeatability and measurement technique's accuracy resulted in the detection of a 1 mm internal void, a 2 mm scaling attack, and a 1 mm skewing attack. Additionally, for a short to moderate length build of an impeller model, it was possible to detect a 0.25 mm change in the fin base thickness. A second case study is also presented wherein dogbone tensile test coupons were manufactured on a material extrusion system at different extrusion temperatures. This process parameter is an example of a setting that can be maliciously modified and have an effect on the final part strength without the operator's knowledge. The performance characteristics (Young's modulus and maximum stress) were determined to be statistically different at different extrusion temperatures (235 and 270 °C). / Master of Science / Additive Manufacturing (AM, also known as 3D printing) machines are cyber-physical systems and are therefore vulnerable to malicious attacks that can cause physical damage to the parts being manufactured or even to the machine itself. This thesis proposes a new method for detecting that an AM system has been hacked. Attacks are identified via a series of measurements taken by a measurement system that is disconnected from the main network. The disconnection ensures that if the network and/or AM system are hacked, the manufacturer can still rely on the measurement system for attack detection. The proposed method uses a physical hash to transfer information to the disconnected measurement system. This physical hash takes the form of a QR code and stores in it the nominal process parameters and toolpath of the build. It is manufactured alongside the original geometry for the measurement system to scan and compare to the readings from its sensor suite. By taking measurements in real-time, the measurement system can detect if the part being manufactured matches the designer’s specification. A proof-of-concept of the proposed method was realized on a common AM system. The implementation was successful and demonstrated the ability of this method to detect the existence of a malicious attack. A case study for detecting changes to the toolpath is also proposed using the simple measurement of how long each layer takes to build. Given benchmark readings of a part manufactured on the same technology as the proof-of-concept implementation, several modifications were able to be detected. The attack types tested were the insertion of an internal void, scaling the part, and skewing the part. A second case study is also presented where components were manufactured at different extrusion temperatures. By measuring the force required to break the parts, it was determined that temperature has an effect on the final part strength. This confirmed that malicious attacks targeting extrusion temperature are a plausible threat, and that the parameter should be measured in the proposed system.

The integration of Artificial Intelligence (AI) into water supply systems (WSSs) has revolutionized real-time monitoring, automated operational control, and predictive decision-making analytics. However, AI also introduces security vulnerabilities, such as data poisoning. In this context, data poisoning could involve the malicious manipulation of critical data, including water quality parameters, flow rates, and chemical composition levels. The consequences of such threats are significant, potentially jeopardizing public safety and health due to decisions being made based on poisoned data. This thesis aims to exploit these vulnerabilities in data-driven applications within WSSs. Proposing Water Generative Adversarial Networks, H2OGAN, a time-series GAN-based model designed to synthesize water data. H2OGAN produces water data based on the characteristics within the expected constraints of water data cardinality. This generative model serves multiple purposes, including data augmentation, anomaly detection, risk assessment, cost-effectiveness, predictive model optimization, and understanding complex patterns within water systems. Experiments are conducted in AI and Cyber for Water and Agriculture (ACWA) Lab, a cyber-physical water testbed that generates datasets replicating both operational and adversarial scenarios in WSSs. Identifying adversarial scenarios is particularly importance due to their potential to compromise water security. The datasets consist of 10 physical incidents, including normal conditions, sensor anomalies, and malicious attacks. A recurrent neural network (RNN) model, i.e., gated recurrent unit (GRU), is used to classify and capture the temporal dynamics those events. Subsequently, experiments with real-world data from Alexandria Renew Enterprises (AlexRenew), a wastewater treatment plant in Alexandria, Virginia, are conducted to assess the effectiveness of H2OGAN in real-world applications. / Master of Science / Today, a significant portion of the global population struggles with access to essential services: 25% lack clean water, 50% lack sanitation services, and 30% lack hygiene facilities. In response, AI is being leveraged to tackle these deficiencies within water supply systems. Investments in AI are expected to reach an estimated $6.3 billion by 2030, with potential savings of 20% to 30% in operational expenditures by optimizing chemical usage in water treatment. The flexibility and efficiency of AI applications have fueled optimism about their potential to revolutionize water management. As the era of Industry 4.0 progresses, the role of AI in transforming critical infrastructures, including water supply systems, becomes increasingly vital. However, this technological integration brings with it heightened vulnerabilities. The water sector, recognized as one of the 16 critical infrastructures by the Cybersecurity and Infrastructure Security Agency (CISA), has seen a notable increase in cyberattack incidents. These attacks underscore the urgent need for sophisticated AI-driven security solutions to protect these essential systems against potential compromises that could pose significant public health risks. Addressing these challenges, this thesis introduces H2OGAN, a time-series GAN-based model developed to generate and analyze realistic water data within the expected constraints of water parameter characteristics. H2OGAN supports various functions including data augmentation, anomaly detection, risk assessment, and predictive model optimization, thereby enhancing the security and efficiency of water supply systems. Extensive testing is conducted in ACWA Lab, a cyber-physical testbed that replicates both operational and adversarial scenarios. These experiments utilize a RNN model, specifically a GRU, to classify and analyze the dynamics of various scenarios including normal operations, sensor anomalies, and malicious attacks. Further real-world validation is carried out at AlexRenew, a wastewater treatment facility in Alexandria, Virginia, confirming the effectiveness of H2OGAN in practical applications. This research not only advances the understanding of AI in water management but also emphasizes the critical need for robust security measures to protect against the evolving landscape of cyber threats.

In the era of experience economy, how best to deliver memorable and exciting customer experiences has become a key issue for service providers. This research aims to build a systematical, quantitative and expectation-based mechanism to design and manage service experience and operation for cyber-physical services. Consequently, this study not only analyzes and synthesizes the critical factors by reviewing literatures (that is, customer expectation, service operation and customer emotion) within the background of service science but also establishes a conceptual theoretical framework for designing satisfactory service experiences. Furthermore, this study presents a concept of the Exquisite Technology and a service system (i.e. U2EX) with a customer expectation management engine (including five core methods, Hawk-Dove game, Coopetition, PSO, FCM and expectation measurement model) in the exhibition context to demonstrate the feasibility of implementing the notions of customer expectation management and service experience design. Besides, we integrate the expectation theory with the emotion theory to build a theoretical concept and employ a multimethod (including a single case study, interviews, simulations and questionnaire surveys) to test the relations and research propositions of the theoretical concept. The research results show positive evidences to support our developed theoretical concept. The customer expectation measurement model is one critical element of the proposed engine that can help service providers understand and quantify customer expectation in dynamic and real time environments for appropriate service experiences based on the systematical and theoretical groundings (i.e. Fechner’s law and operation risk). Hence, we use the simulations to verify the reliability of the customer expectation measurement model. Meanwhile, this research also conducts simulation experiments of Hawk-Dove game, PSO, FCM and Coopetition methods to have preliminary evidences for supporting the proposed mechanism. Thus, service providers provide customers with high-quality service experiences to achieve customer satisfaction and co-create values with customers through meticulous service experiences design approaches. The proposed mechanism of expectation-based service experience and operation design and management has been demonstrated in the exhibition service sector. We would like to apply the advantage and usage of the proposed mechanism to the other feasible domains and service sectors. Consequently, this study proposes a S-D based input-output analysis approach in order to find the potential fields that can also adopt the proposed mechanism by measuring the effects of technology spillovers.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-05-09T17:24:59Z No. of bitstreams: 1 LENARDO CHAVES E SILVA - TESE PPGCC 2015..pdf: 9863003 bytes, checksum: b4ff7a7517f3ec159596b4b3c8730219 (MD5) / Made available in DSpace on 2018-05-09T17:24:59Z (GMT). No. of bitstreams: 1 LENARDO CHAVES E SILVA - TESE PPGCC 2015..pdf: 9863003 bytes, checksum: b4ff7a7517f3ec159596b4b3c8730219 (MD5) Previous issue date: 2015-11-12 / Capes / Sistemas Médicos Físico-Cibernéticos (SMFC) são sistemas críticos cientes de contexto que têm a segurança do paciente como principal requisito, demandando processos rigorosos de validação para garantir a conformidade com os requisitos do usuário e a corretude orientada à especificação. Neste trabalho é proposta uma arquitetura baseada em modelos para validação de SMFC, focando em promover a reúso e a produtividade. Tal abordagem permite que desenvolvedores de sistemas construam modelos formais de SMFC baseados em uma biblioteca de modelos de pacientes e dispositivos médicos, bem como simular o SMFC para identificar comportamentos indesejados em tempo de projeto. A abordagem proposta foi aplicada a três diferentes cenários clínicos para avaliar seu potencial de reúso para diferentes contextos. A abordagem foi também validada por meio de uma avaliação empírica com desenvolvedores para avaliar o reúso e a produtividade. Finalmente, os modelos foram formalmente verificados considerando os requisitos funcionais e de segurança, além da cobertura dos modelos. / Medical Cyber-Physical Systems (MCPS) are context-aware, life-critical systems with patient safety as the main concern, demanding rigorous processes for validation to guarantee user requirement compliance and specification-oriented correctness. In this article, we propose a model-based approach for early validation of MCPS, focusing on promoting reusability and productivity. It enables system developers to build MCPS formal models based on a library of patient and medical device models, and simulate the MCPS to identify undesirable behaviors at design time. Our approach has been applied to three different clinical scenarios to evaluate its reusability potential for different context. We have also validated our approach through an empirical evaluation with developers to assess productivity and reusability. Finally, our models have been formally verified considering functional and safety requirements and model coverage.

Idag pratas det mycket om smart teknik och man säger att den fjärde industriella revolutionen är på väg. Revolutionen kallas för Industri 4.0 och innebär två tekniska förbättringar, Internet of Things (IoT) och Cyber-Physical Systems (CPS). IoT låter fysiska enheter sammankopplas i ett system med andra enheter med hjälp av elektromagnetiska vågor och CPS ger möjligheten till att få in information från omvärlden och implementera informationen i digital form. När det kommer till implementering i tillverkningsindustrin används begreppen Industrial Internet of Things och Cyber-Physical Production Systems. Arbetet består av en djupgående litteraturstudie och undersöker vad implementering av IoT och CPS i personbilsbranschens tillverkningssystem kan leda till och hur de fungerar i praktiken. Teorin utgår från vetenskapliga artiklar, tidskrifter och journaler samt en studie från Atlas Copco. Eftersom att smart teknik är ett brett ämne och vi behövde förhålla oss till en tidsgräns på 18 veckor avgränsades arbetet till endast IoT och CPS i tillverkande personbilsföretag. Branschen för personbilar valdes för att i jämförelse med andra branscher är både kvaliteten och kvantiteten avgörande. Samtidigt som det produceras många personbilar måste varje personbil uppfylla en rad olika krav och varje enhet utgör en betydande del av kapitalet i företaget. Resultatet visar hur IoT och CPS fungerar som helhet och vad för positiva och negativa konsekvenser implementering av begreppen ger. Av resultatet framgår också att faktorerna produktion, ekonomi och människa ska analyseras som en helhet och inte enskilt för att implementeringen ska vara framgångsrik i tillverkande personbilsföretag. Möjligheterna som IoT och CPS medför är snabbare och exaktare beslut, systemövervakning och insamling, utbyte och analysering av data för personbilsbranschens företag. Den största utmaningen som implementeringen av begreppen medför är datahantering. Det finns en risk att oönskade mottagare får tillgång till konfidentiell information genom bland annat dataläckage och dataintrång. Således bör fokus ligga på att förebygga detta för att få ut fördelarna och samtidigt reducera nackdelarna. Slutsatsen som kan dras av resultatet är att en kombination av IoT och CPS i personbilsbranschens tillverkningssystem skapar ett kommunikationsnätverk bland heterogena enheter som gör att system kan kommunicera och utbyta data med varandra på ett effektivt sätt. Implementering av begreppen leder till minskning av defekter, introduktionskostnader, energianvändning och upplärning för arbetare samt ökad verktygsdrift och produktivitet. / Today, there is much talk about smart technology and it is said that the fourth industrial revolution is on its way. The revolution is called Industry 4.0 and involves two technical improvements, the Internet of Things (IoT) and Cyber-Physical Systems (CPS). IoT allows physical devices to be interconnected in a system with other devices using electromagnetic waves and CPS provides the opportunity to get information from the outside world and implement the information in digital form. When it comes to implementation in the manufacturing industry, the concepts Industrial Internet of Things and Cyber-Physical Production Systems are used. The thesis consists of an in-depth literature study and investigates what implementation of IoT and CPS in the automotive industry's manufacturing system can lead to and how they work in practice. The theory is based on scientific articles, paper and journals, and a study by Atlas Copco. Because smart technology is a broad topic and we needed to relate to a time limit of 18 weeks, the work was limited to IoT and CPS only in manufacturing passenger car companies. The industry for passenger cars was chosen so that, in comparison with other industries, both the quality and the quantity are decisive. While many passenger cars are being produced, each passenger car must meet a variety of requirements and each unit constitutes a significant part of the capital of the company. The result shows how IoT and CPS work as a whole and what positive and negative consequences the implementation of the concepts gives. The result also shows that the factors of production, economy and humanity should be analysed as a whole and not individually in order for the implementation to be successful in manufacturing passenger car companies. The opportunities that IoT and CPS entail are faster and more precise decisions, system monitoring and collection, exchange and analysis of data for the automotive industry's companies. The biggest challenge that the implementation of the concepts entails is data management. There is a risk that unwanted recipients will have access to confidential information through, among other things, data leakage and hacking. Thus, the focus should be on preventing this in order to get the benefits and at the same time reduce the disadvantages. The conclusion that can be drawn from the result is that IoT and CPS in the automotive industry's manufacturing system create a communication network among heterogeneous units that enable systems to communicate and exchange data with each other in an efficient manner. Implementation of the concepts leads to a reduction of defects, introduction costs, energy use and training for workers, as well as increased tool operation and productivity.

Nous abordons des problèmes de sécurité dans des systèmes cyber-physiques industriels. Les attaques contre ces systèmes doivent être traitées à la fois en matière de sûreté et de sécurité. Les technologies de contrôles imposés par les normes industrielles, couvrent déjà la sûreté. Cependant, du point de vue de la sécurité, la littérature a prouvé que l’utilisation de techniques cyber pour traiter la sécurité de ces systèmes n’est pas suffisante, car les actions physiques malveillantes seront ignorées. Pour cette raison, on a besoin de mécanismes pour protéger les deux couches à la fois. Certains auteurs ont traité des attaques de rejeu et d’intégrité en utilisant une attestation physique, p. ex., le tatouage des paramètres physiques du système. Néanmoins, ces détecteurs fonctionnent correctement uniquement si les adversaires n’ont pas assez de connaissances pour tromper les deux couches. Cette thèse porte sur les limites mentionnées ci-dessus. Nous commençons en testant l’efficacité d’un détecteur qui utilise une signature stationnaire afin de détecter des actions malveillantes. Nous montrons que ce détecteur est incapable d’identifier les adversaires cyber-physiques qui tentent de connaître la dynamique du système. Nous analysons son ratio de détection sous la présence de nouveaux adversaires capables de déduire la dynamique du système. Nous revisitons le design original, en utilisant une signature non stationnaire, afin de gérer les adversaires visant à échapper à la détection. Nous proposons également une nouvelle approche qui combine des stratégies de contrôle et de communication. Toutes les solutions son validées à l’aide de simulations et maquettes d’entraînement / We address security issues in cyber-physical industrial systems. Attacks against these systems shall be handled both in terms of safety and security. Control technologies imposed by industrial standards already cover the safety dimension. From a security standpoint, the literature has shown that using only cyber information to handle the security of cyber-physical systems is not enough, since physical malicious actions are ignored. For this reason, cyber-physical systems have to be protected from threats to their cyber and physical layers. Some authors handle the attacks by using physical attestations of the underlying processes, f.i., physical watermarking to ensure the truthfulness of the process. However, these detectors work properly only if the adversaries do not have enough knowledge to mislead crosslayer data. This thesis focuses on the aforementioned limitations. It starts by testing the effectiveness of a stationary watermark-based fault detector, to detect, as well, malicious actions produced by adversaries. We show that the stationary watermark-based detector is unable to identify cyber-physical adversaries. We show that the approach only detects adversaries that do not attempt to get any knowledge about the system dynamics. We analyze the detection performance of the original design under the presence of adversaries that infer the system dynamics to evade detection. We revisit the original design, using a non-stationary watermark-based design, to handle those adversaries. We also propose a novel approach that combines control and communication strategies. We validate our solutions using numeric simulations and training cyber-physical testbeds

In the era of Industry 4.0, the Internet-of-Things (IoT) plays the driving role comparable to steam power in the first industrial revolution. IoT provides the potential to combine machine-to-machine (M2M) interaction and real time data collection within the field of manufacturing. Therefore, the adoption of IoT in industry enhances dynamic optimization, control and data-driven decision making. However, the domain suffers due to interoperability issues, with massive numbers of IoT devices connecting to the internet despite the absence of communication standards upon. Heterogeneity is pervasive in IoT ranging from the low levels (device connectivity, network connectivity, communication protocols) to high levels (services, applications, and platforms). The project investigates the current state of industrial IoT (IIoT) ecosystem, to draw a comprehensive understanding on interoperability challenges and current solutions in supporting of IoT-based smart manufacturing. Based upon a literature review, IIoT interoperability issues were classified into four levels: technical, syntactical, semantic, and organizational level interoperability. Regarding each level of interoperability, the current solutions that addressing interoperability were grouped and analyzed. Nine reference architectures were compared in the context of supporting industrial interoperability. Based on the analysis, interoperability research trends and challenges were identified. FIWARE Generic Enablers (FIWARE GEs) were identified as a possible solution in supporting interoperability for manufacturing applications. FIWARE GEs were evaluated with a scenario-based Method for Evaluating Middleware Architectures (MEMS).  Nine key scenarios were identified in order to evaluate the interoperability attribute of FIWARE GEs. A smart manufacturing use case was prototyped and a test bed adopting FIWARE Orion Context Broker as its main component was designed. The evaluation shows that FIWARE GEs meet eight out of nine key scenarios’ requirements. These results show that FIWARE GEs have the ability to enhance industrial IoT interoperability for a smart manufacturing use case. The overall performance of FIWARE GEs was also evaluated from the perspectives of CPU usage, network traffic, and request execution time. Different request loads were simulated and tested in our testbed. The results show an acceptable performance in terms with a maximum CPU usage (on a Macbook Pro (2018) with a 2.3 GHz Intel Core i5 processor) of less than 25% with a load of 1000 devices, and an average execution time of less than 5 seconds for 500 devices to publish their measurements under the prototyped implementation. / I en tid präglad av Industry 4.0, Internet-of-things (IoT) spelar drivande roll jämförbar med ångkraft i den första industriella revolutionen. IoT ger potentialen att kombinera maskin-till-maskin (M2M) -interaktion och realtidsdatainsamling inom tillverkningsområdet. Därför förbättrar antagandet av IoT i branschen dynamisk optimering, kontroll och datadriven beslutsfattande. Domänen lider dock på grund av interoperabilitetsproblem, med enorma antal IoT-enheter som ansluter till internet trots avsaknaden av kommunikationsstandarder på. Heterogenitet är genomgripande i IoT som sträcker sig från de låga nivåerna (enhetskonnektivitet, nätverksanslutning, kommunikationsprotokoll) till höga nivåer (tjänster, applikationer och plattformar). Projektet undersöker det nuvarande tillståndet för det industriella IoT (IIoT) ekosystemet, för att få en omfattande förståelse för interoperabilitetsutmaningar och aktuella lösningar för att stödja IoT-baserad smart tillverkning. Baserat på en litteraturöversikt klassificerades IIoT-interoperabilitetsfrågor i fyra nivåer: teknisk, syntaktisk, semantisk och organisatorisk nivå interoperabilitet. När det gäller varje nivå av driftskompatibilitet grupperades och analyserades de nuvarande lösningarna för adressering av interoperabilitet. Nio referensarkitekturer jämfördes i samband med att stödja industriell driftskompatibilitet. Baserat på analysen identifierades interoperabilitetstrender och utmaningar. FIWARE Generic Enablers (FIWARE GEs) identifierades som en möjlig lösning för att stödja interoperabilitet för tillverkningstillämpningar. FIWARE GEs utvärderades med en scenariebaserad metod för utvärdering av Middleware Architectures (MEMS). Nio nyckelscenarier identifierades för att utvärdera interoperabilitetsattributet för FIWARE GEs. Ett smart tillverkningsfodral tillverkades med prototyper och en testbädd som antog FIWARE Orion Context Broker som huvudkomponent designades. Utvärderingen visar att FIWARE GE uppfyller åtta av nio krav på nyckelscenarier. Dessa resultat visar att FIWARE GE har förmågan att förbättra industriell IoT-interoperabilitet för ett smart tillverkningsfodral. FIWARE GEs totala prestanda utvärderades också utifrån perspektivet för CPU-användning, nätverkstrafik och begär exekveringstid. Olika förfrågningsbelastningar simulerades och testades i vår testbädd. Resultaten visar en acceptabel prestanda i termer av en maximal CPU-användning (på en Macbook Pro (2018) med en 2,3 GHz Intel Core i5-processor) på mindre än 25% med en belastning på 1000 enheter och en genomsnittlig körningstid på mindre än 5 sekunder för 500 enheter att publicera sina mätningar under den prototyperna implementateringen.