A networked control system (NCS) is a feedback control system that has its control loop physically connected via real-time communication networks. To meet the demands of `teleautomation', modularity, integrated diagnostics, quick maintenance and decentralization of control, NCSs have received remarkable attention worldwide during the past decade. Yet despite their distinct advantages, NCSs are suffering from network-induced constraints such as time delays and packet dropouts, which may degrade system performance. Therefore, the network-induced constraints should be incorporated into the control design and related studies.<p> For the problem of state estimation in a network environment, we present the strategy of simultaneous input and state estimation to compensate for the effects of unknown input missing. A sub-optimal algorithm is proposed, and the stability properties are proven by analyzing the solution of a Riccati-like equation.<p> Despite its importance, system identification in a network environment has been studied poorly before. To identify the parameters of a system in a network environment, we modify the classical Kalman filter to obtain an algorithm that is capable of handling missing output data caused by the network medium. Convergence properties of the algorithm are established under the stochastic framework.<p> We further develop an adaptive control scheme for networked systems. By employing the proposed output estimator and parameter estimator, the designed adaptive control can track the expected signal. Rigorous convergence analysis of the scheme is performed under the stochastic framework as well.

A networked control system (NCS) is a feedback control system that has its control loop physically connected via real-time communication networks. To meet the demands of `teleautomation', modularity, integrated diagnostics, quick maintenance and decentralization of control, NCSs have received remarkable attention worldwide during the past decade. Yet despite their distinct advantages, NCSs are suffering from network-induced constraints such as time delays and packet dropouts, which may degrade system performance. Therefore, the network-induced constraints should be incorporated into the control design and related studies.<p> For the problem of state estimation in a network environment, we present the strategy of simultaneous input and state estimation to compensate for the effects of unknown input missing. A sub-optimal algorithm is proposed, and the stability properties are proven by analyzing the solution of a Riccati-like equation.<p> Despite its importance, system identification in a network environment has been studied poorly before. To identify the parameters of a system in a network environment, we modify the classical Kalman filter to obtain an algorithm that is capable of handling missing output data caused by the network medium. Convergence properties of the algorithm are established under the stochastic framework.<p> We further develop an adaptive control scheme for networked systems. By employing the proposed output estimator and parameter estimator, the designed adaptive control can track the expected signal. Rigorous convergence analysis of the scheme is performed under the stochastic framework as well.

The complexity of networked systems has been continuously growing, and the abundance of online resources has presented practical management challenges. Specifically, system administrators are required to carefully configure their online systems to minimize security vulnerabilities of resource management, including resource creation, maintenance, and disposal. However, numerous networked systems have been exploited or compromised by adversaries, due to misconfiguration and mismanagement of human errors. In this dissertation, we explore different network systems to identify security vulnerabilities that adversaries could exploit for malicious purposes. First, we investigate the identity-account inconsistency threat, a new SSO vulnerability that can cause the compromise of online accounts. We demonstrate that this inconsistency in SSO authentication allows adversaries controlling a reused email address to take over online accounts without using any credentials. To substantiate our findings, we conduct a measurement study on the account management policies of various cloud email providers, highlighting the feasibility of acquiring previously used email accounts. To gain insight into email reuse in the wild, we examine commonly employed naming conventions that contribute to a significant number of potential email address collisions. To mitigate the identity-account inconsistency threat, we propose a range of useful practices for end-users, service providers, and identity providers. Secondly, we present a comprehensive study on the vulnerability of container registries to typosquatting attacks. In typosquatting attacks, adversaries intentionally upload malicious container images with identifiers similar to those of benign images, leading users to inadvertently download and execute malicious images. Our study demonstrates that typosquatting attacks can pose a significant security threat across public and private container registries, as well as across multiple platforms. To mitigate the typosquatting attacks in container registries, we propose CRYSTAL, a lightweight extension to the existing Docker command-line interface. Thirdly, we present an in-depth study on hardware resource management in cloud gaming services. Our research uncovers that adversaries can intentionally inject malicious programs or URLs into these services using game mods. To demonstrate the severity of these vulnerabilities, we conduct four proof-of-concept attacks on cloud gaming services, including crypto-mining, machine-learning model training, Command and Control, and censorship circumvention. In response to these threats, we propose several countermeasures that cloud gaming services can implement to safeguard their valuable assets from malicious exploitation. These countermeasures aim to enhance the security of cloud gaming services and mitigate the security risks associated with hardware mismanagement. Last but not least, we present a comprehensive and systematic study on NXDomain, examining its scale, origin, and security implications. By leveraging a large-scale passive DNS database, we analyze a vast dataset spanning from 2014 to 2022, identifying an astonishing 146 trillion NXDomains queried by DNS users. To gain further insights into the usage patterns and security risks associated with NXDomains, we carefully select and register 19 NXDomains in the DNS database. To analyze the behavior and sources of these queries, we deploy a honeypot for our registered domains and collect 5,925,311 queries over a period of six months. Furthermore, we conduct extensive traffic analysis on the collected data, uncovering various malicious uses of NXDomains, including botnet takeovers, malicious file injections, and exploitation of residual trust. / Doctor of Philosophy / This dissertation investigates the security risks arising from resource management in various network systems. On the one hand, we explore the security risks of software resource mismanagement, examining two specific threats: the identity-account inconsistency threat in Single Sign-On authentication schemes and the typosquatting attack in container registries. On the other hand, we investigate hardware resource misuse in network systems, focusing on two security issues: the exploitation of computing hardware in cloud gaming services and the analysis of NXDomains within the Domain Name System (DNS). By thoroughly analyzing and understanding these security risks, this dissertation contributes to the advancement of networked system security and provides necessary countermeasures to protect Internet users against these threats.

Les systèmes de contrôle en réseau sont un champ actif de recherche, où les différentes composantes du réseau sont spatialement distribué et tentent d'atteindre un objectif global. Ils apparaissent naturellement lors l'interaction d'un système piloté par ordinateur avec le monde physique.Avec les systèmes de contrôle en réseau une classe connexe des systèmes est décrit par les systèmes Cyber-physique, où les capacités de calcul embarqué peuvent interagir avec le monde physique.Dans ce travail, nous allons considérer la tâche classique d'observation et d'estimation et étudier les cas où les contraintes induite par le réseau nécessite une adaptation des mécanismes classique d'observation et d'estimation.Dans les système de contrôle en raison de limitation des capteurs (pour des raisons pratiques telles que la réduction des coûts) certains états ou paramètre du système ne sont pas connus. Dans ce contexte, la notion classique d'observabilitéexprime la capacité de déduire de la mesure les valeurs d'intérêt.Premièrement nous considérons le problème de la réduction de l'échantillonnage par l'utilisation de échantillonnage événementiel et ce pour plusieurs classes de systèmes. Ensuite, une procédure d'estimation et de contrôle sera proposé pour résoudre le problème du consensus dans un système multi-agent.Considérant enfin une dynamique de véhicule plus complexe, nous nous concentrons sur l'estimation du coefficient de frottement de la route pour résoudre un problème de suivi. / Network control systems is an active field of study where interacting component spatially distributed try to achieve a global goal. They naturally emerge from the interaction of computer driven mechanism and the physical world.Along with network control system a related class of systems is described by the so called: Cyber-physical systems, where integrated physical computational capabilities can interact.In this work we will consider the classical task of observation and estimation and investigate cases where network induced constraint calls for adapted observation and estimation scheme.In control system due to limitation in sensors ( for practical reason such as cost reduction) all the value of interest (whether the some unmeasured state or unknown parameter)are unknown. The classical notion of observabilityaccount for the ability to deduce from measurement those value of interest.First sampling reduction by use of event trigger will be studied for several class of systems. Then an estimation and control scheme will be establish to solve the problem of consensus in a multi agents system.Finally considering a more complex vehicle dynamic we focus on the estimation of tire road friction coefficient to solve a tracking problem.

Numerous control and decision problems in networked systems can be posed as optimization problems. Examples include the framework of network utility maximization for resource allocation in communication networks, multi-agent coordination in robotics, and collaborative estimation in wireless sensor networks (WSNs). In contrast to classical distributed optimization, which focuses on improving computational efficiency and scalability, these new applications require simple mechanisms that can operate under limited communication. In this thesis, we develop several novel mechanisms for distributed optimization under communication constraints, and apply these to several challenging engineering problems. In particular, we devise three tailored optimization algorithms relying only on nearest neighbor, also known as peer-to-peer, communication. Two of the algorithms are designed to minimize a non-smooth convex additive objective function, in which each term corresponds to a node in a network. The first method is an extension of the randomized incremental subgradient method where the update order is given by a random walk on the underlying communication graph, resulting in a randomized peer-to-peer algorithm with guaranteed convergence properties. The second method combines local subgradient iterations with consensus steps to average local update directions. The resulting optimization method can be executed in a peer-to-peer fashion and analyzed using epsilon-subgradient methods. The third algorithm is a center-free algorithm, which solves a non-smooth resource allocation problem with a separable additive convex objective function subject to a constant sum constraint. Then we consider cross-layer optimization of communication networks, and demonstrate how optimization techniques allow us to engineer protocols that mimic the operation of distributed optimization algorithms to obtain an optimal resource allocation. We describe a novel use of decomposition methods for cross-layer optimization, and present a flowchart that can be used to categorize and visualize a large part of the current literature on this topic. In addition, we devise protocols that optimize the resource allocation in frequency-division multiple access (FDMA) networks and spatial reuse time-division multiple access (TDMA) networks, respectively. Next we investigate some variants of the consensus problem for multi-robot coordination, for which it is usually standard to assume that agents should meet at the barycenter of the initial states. We propose a negotiation strategy to find an optimal meeting point in the sense that the agents' trajectories to the meeting point minimize a quadratic cost criterion. Furthermore, we also demonstrate how an augmented state vector can be used to boost the convergence rate of the standard linear distributed averaging iterations, and we present necessary and sufficient convergence conditions for a general version of these iterations. Finally, we devise a generic optimization software component for WSNs. To this end, we implement some of the most promising optimization algorithms developed by ourselves and others in our WSN testbed, and present experimental results, which show that the proposed algorithms work surprisingly well. / QC 20100813

Scalable system monitoring is a fundamental abstraction for large-scale networked systems. The goal of this dissertation is to design and build a scalable monitoring middleware that provides system introspection for large distributed systems and that will facilitate the design, development, and deployment of distributed monitoring applications. This middleware will enable monitoring applications to flexibly control the tradeoff between result precision and communication cost and to improve result accuracy in the face of node failures, network delays, and system reconfigurations. We present PRISM (PRecision-Integrated Scalable Monitoring), a scalable monitoring middleware that provides a global aggregate view of large-scale networked systems and that can serve as a building block for a broad range of distributed monitoring applications by coordinating views of multiple vantage points across the network. To coordinate a global view for system introspection, PRISM faces two key challenges: (1) scalability to large systems and high data volumes and (2) safeguarding accuracy in the face of node and network failures. To address these challenges, we design, implement, and evaluate PRISM, a system that defines precision as a new unified abstraction to enable scalable monitoring. PRISM quantifies (im)precision along a three-dimensional vector: arithmetic imprecision (AI) and temporal imprecision (TI) balance precision against monitoring overhead for scalability while network imprecision (NI) addresses the challenge of providing consistency guarantees despite failures. Our prototype implementation of PRISM addresses the challenge of providing these metrics while scaling to a large number of nodes and attributes by (1) leveraging Distributed Hash Tables (DHTs) to create scalable aggregation trees, (2) self-tuning AI budgets across nodes in a principled, near-optimal manner to shift precision to where it is useful, (3) pipelining TI delays across tree levels to maximize batching of updates, and (4) applying dual-tree prefix aggregation which exploits symmetry in our DHT topology to drastically reduce the cost of the active probing needed to maintain NI. Through extensive simulations and experiments on four large-scale testbeds, we observe that PRISM provides a key substrate for scalable monitoring by (1) reducing monitoring load by up to two orders of magnitude compared to existing approaches, (2) providing a flexible framework to control the tradeoff between accuracy, bandwidth cost, and response latency, (3) characterizing and improving confidence in the accuracy of results in the face of system disruptions, and (4) improving the observed accuracy by up to an order of magnitude despite churn. We have built several monitoring applications on top of PRISM including a distributed heavy hitter detection service, a distributed monitoring service for Internet-scale systems, and a detection service for monitoring distributed-denial-of-service (DDoS) attacks at the source-side in distributed networked systems. Finally, we demonstrate how the unified precision abstraction enables new monitoring applications by presenting experiences from these applications. / text

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<p>The design and discovery of novel materials are difficult not only due to expensive and time- consuming calculation and measurements of their properties, but also thanks to the infinite search spaces. With the increasingly abundant data from experiments and simulations, learning from data has the potential of bypassing complex physics-based simulations and experiments and providing fast approximations of the solution. Deep learning models are helpful in the design process that requires prohibitively expensive iterative computations. In addition, as efficient and accurate sur- rogate models, trained deep networks can incorporate techniques, such as sensitivity analysis and active learning, to provide guidance in searching promising candidates. Moreover, deep learning models need to account for the material structural information, such as molecule and atom align- ments, chemical bonds, and grain-level interactions, as it plays an important role in determining the macroscopic properties. In this thesis, we start with developing two standard deep learning model- based materials design frameworks for lithium-ion batteries and thermoelectric materials, and we then investigate the feasibility of standard deep learning models on data with graph-structured in- formation and identify the challenges. Finally, we propose a deep graph operator network that effectively capture the spatial dependency encoded in the graph structure to solve networked dy- namical systems.</p> <p><br></p> <p>In the first half of the thesis, we propose a hybrid convolutional neural network to infer lithium- ion battery microstructure properties, Bruggeman’s exponent and shape factor, given its voltage vs. capacity curves. The trained model accurately predicts the microstructural properties on both experimental and simulation data, and it can readily accelerate the processing-properties- performance and degradation characteristics of the existing and emerging chemistries of lithium- ion batteries. Also, we develop a AI-guided framework to discover and design thermoelectric materials, where we train classifiers based on the materials chemical and structural information embeddings and combine with variance-based sensitivity analysis to suggest candidates and con- duct fast screening.</p> <p><br></p> <p>In the second half of the thesis, we build a data-centric framework with a recurrent neural network-based classifier to achieve traffic incident detection on highway networks. We incorporate weak supervised learning and design labeling functions to create large amount of training data with probabilistic labels. The trained deep ensemble accurately detects incidents with predictive uncertainty. To capture the structural information in the network, we then propose a deep graph operator network that maps the input graph state function to the output graph state function. The proposed model enables resolution-independence and zero-shot transfer, where we do not require a set of fixed sensors to encode the graph trajectory and can use the trained model directly on larger graphs with high accuracy. We utilize the proposed model to solve power grid transient stability prediction and traffic forecasting problems.</p>

Networked systems have attracted great interests from the control society during the last decade. Several issues rising from the recent research are addressed in this dissertation. Connectedness is one of the important conditions that enable distributed coordination in a networked system. Nonetheless, it has been assumed in most implementations, especially in continuous-time applications, until recently. A nonlinear weighting strategy is proposed in this dissertation to solve the connectedness preserving problem. Both rendezvous and formation problem are addressed in the context of homogeneous network. Controllability of heterogeneous networks is another issue which has been long omitted. This dissertation contributes a graph theoretical interpretation of controllability. Distributed sensor networks make up another important class of networked systems. A novel estimation strategy is proposed in this dissertation. The observability problem is raised in the context of our proposed distributed estimation strategy, and a graph theoretical interpretation is derived as well. The contributions of this dissertation are as follows: It solves the connectedness preserving problem for networked systems. Based on that, a formation process is proposed. For heterogeneous networks, the leader-follower structure is studied and sufficient and necessary conditions are presented for the system to be controllable. A novel estimation strategy is proposed for distributed sensor networks, which could improve the performance. The observability problem is studied for this estimation strategy and a necessary condition is obtained. This work is among the first ones that provide graph theoretical interpretations of the controllability and observability issues.

Automatic control techniques have been widely employed in industry to increase efficiency and profitability of the processes. However, reliability on automation increases the susceptibility of the system to faults in major control equipment such as actuators and sensors. This realization has motivated design of frameworks for fault detection and isolation (FDI) and fault tolerant control (FTC). The success of these FDI and FTC mechanisms is contingent on their ability to handle complexities associated with process systems such as nonlinearity, uncertainty, high dimensionality and the resulting effects of the existence of complexity in system structure such as faults that cannot be isolated. Motivated by the above considerations, this thesis considers the problem of fault diagnosis and fault tolerant control for complex process systems. First, an FDI framework is designed that can detect and confine possible locations for faults that cannot be isolated. Next, the problem of simultaneous actuator and sensor fault diagnosis for nonlinear uncertain systems. The key idea is to design FDI filters in a way they account for the impact of uncertainty explicitly. This work then considers the problem of simultaneous fault diagnosis in nonlinear uncertain networked systems. FDI is achieved using a distributed architecture, comprised of a bank of local FDI (LFDI) schemes that communicate with each other. The efficacy of the proposed FDI methodologies is shown via application to a number of chemical process examples. Finally, an integrated framework is proposed for fault diagnosis and fault tolerant control of variable air volume (VAV) boxes, a common component of heating, ventilation and air conditioning (HVAC) systems as an industrial case study of complex systems. The advantages of the proposed framework are diagnosing multiple faults and handling faults in stuck dampers using a safe parking strategy with energy saving capability. / Thesis / Doctor of Philosophy (PhD) / Automation is the key to increase efficiency and profitability of the processes. However, as the level of automation increases, major control equipment are more prone to faults. Thus, fault detection and isolation (FDI) and fault tolerant control (FTC) frameworks are required for fault handling. Fault handling, however, can only be efficiently achieved if the designed FDI and FTC frameworks are able to deal with complexities arising in process systems such as nonlinearity, uncertainty, high dimensionality and the resulting effects of the existence of complexity in system structure such as faults that cannot be isolated. This motivates design of FDI and FTC frameworks for complex process systems. First, FDI frameworks are presented that can diagnose faults in the presence of complexities mentioned above. Then, an integrated framework is designed for diagnosing and handling faults of heating, ventilation and air conditioning (HVAC) systems as an industrial case study of complex process systems.