The number of IoT and cyber-physical systems will be growing in the comingfuture. According to estimates, more than 21 billion IoT devices are expectedto exist by 2025. The adoption of Digital Twins and AI-enhanced IoTapplications is projected to fuel the expected increase in IoT spending.It is essential to accelerate the development., deployment, and administrationof these IoT applications, which can be accomplished by orchestrating IoTcomponents, devices, services, and systems. IoT Intelligence orchestrationposes several obstacles that must be overcome for a wide range of domainspecific use cases and applications to follow and support business logic.This thesis aims to create a secure and full proof way of orchestratingintelligence within IoT devices from multiple ecosystems.It also aims tobreak down the current approach of monolithic development and introduce amixture of visual programming and distributed systems considering all thenecessary cyber-security aspects,.A comparison of the developed framework and the existing tool with thenecessary characteristics like security, response time and the accuracy too isincluded.of the thesis.

The advent of Information and Communications Technology (ICT) in power systems has revolutionized the monitoring, operation, and control mechanisms through advanced control and communication functions. However, this integration significantly elevates the vulnerability of modern power systems to cyber intrusions, posing severe risks to the integrity and reliability of the power grid. This dissertation presents the results of a comprehensive study into the detection of cyber intrusions and restoration of cyber-power systems post-attack with a focus on IEC 61850 based substations and recovery methodologies in the cyber-physical system framework. The first step of this study is to develop a novel Intrusion Detection System (IDS) specifically designed for deployment in automated substations. The proposed IDS effectively identifies falsified measurements within Manufacturing Messaging Specification (MMS) messages by verifying the consistency of electric circuit laws. This distributed approach helps avoid the transfer of contaminated measurements from substations to the control center, ensuring the integrity of SCADA systems. Utilizing a cyber-physical system testbed and the IEEE 39-bus test system, the IDS demonstrates high detection accuracy and validates its efficacy in real-time operational environments. Building upon the intrusion detection methodology, this dissertation advances into cyber system recovery strategies, which are designed to meet the challenges of restoring a power grid as a cyber-physical system following catastrophic cyberattacks. A novel restoration strategy is proposed, emphasizing the self-recovery of a substation automation system (SAS) within the substation through dynamic network reconfiguration and collaborative efforts among Intelligent Electronic Devices (IEDs). This strategy, validated through a cyber-power system testbed incorporating SDN technology and IEC 61850 protocol, highlights the critical role of cyber recovery in maintaining grid resilience. Further, this research extends its methodology to include a cyber-physical system restoration strategy that integrates an optimization-based multi-system restoration approach with cyber-power system simulation for constraint checking. This innovative strategy developed and validated using an Software Defined Networking (SDN) network for the IEEE 39-bus system, demonstrates the capability to efficiently restore the cyber-power system and maximize restoration capability following a large-scale cyberattack. Overall, this dissertation makes original contributions to the field of power system security by developing and validating effective mechanisms for the detection of and recovery from cyber intrusions in the cyber-power system. Here are the main contributions of this dissertation: 1) This work develops a distributed IDS, specifically designed for the substation automation environment, capable of pinpointing the targets of cyberattacks, including sophisticated attacks involving multiple substations. The effectiveness of this IDS in a real-time operational context is validated to demonstrate its efficiency and potential for widespread deployment. 2) A novel recovery strategy is proposed to restore the critical functions of substations following cyberattacks. This strategy emphasizes local recovery procedures that leverage the collaboration of devices within the substation network, circumventing the need for external control during the initial recovery phase. The implementation and validation of this method through a cyber-physical system testbed—specifically, within an IEC 61850 based Substation Automation System (SAS)—underscores its practicality and effectiveness in real-world scenarios. 3) The dissertation results in a new co-restoration strategy that integrates mixed integer linear programming to sequentially optimize the restoration of generators, power components, and communication nodes. This approach ensures optimal restoration decisions within a limited time horizon, enhancing the recovery capabilities of the cyber-power system. The application of an SDN based network simulator facilitates accurate modeling of cyber-power system interactions, including communication constraints and dynamic restoration scenarios. The strategy's adaptability is further improved by real-time assessment of the feasibility of the restoration sequence incorporating power flow and communication network constraints to ensure an effective recovery process. / Doctor of Philosophy / Electricity is a critical service that supports the society and economy. Today, electric power systems are becoming smarter, using advanced Information and Communications Technology to manage and distribute electricity more efficiently. This new technology creates a smart grid, a network that not only delivers power but also uses computers and other tools to remotely monitor electricity flows and address any issues that may arise. However, these smart systems with high connectivity utilizing information and communication systems can be vulnerable to cyberattacks, which could disrupt the electricity supply. To protect against these threats, this study is focused on creating systems that can detect when an abnormal condition is taking place in the cyber-power grid. These detection systems are designed to detect and identify signs of cyberattacks at key points in the power network, particularly at substations, which play a vital role in the delivery of electricity. Substations control the power grid operating conditions to make sure that electricity service is reliable and efficient for the consumers Just like traffic lights help manage the flow of vehicles, substations manage the flow of electricity to make sure electric energy is delivered to where it needed. Once a cyberattack is detected, the next step is to stop the attack and mitigate the impact it may have made to ensure that the power grid returns to normal operations as quickly as possible. This dissertation is concerned with the development and validation of analytical and computational methods to quickly identify the cyberattacks and prevent the disruptions to the electricity service. Also, the focus of this work is also on a coordinated recovery of both the cyber system ( digital controls and monitoring) and power system (physical infrastructure including transformers and transmission and distribution lines). This co-restoration approach is key to sustain the critical electricity service and ensures that the grid is resilient against the cyber threats. By developing strategies that address both the cyber and physical aspects, the proposed methodology aims to minimize downtime and reduce the impact of large-scale cyberattacks on the electrical infrastructure. The impact of the results of this dissertation is the enhancement of security and resilience of the electric energy supply in an era where the risks of cyber threats are increasingly significantly. Overall, by developing new methodologies to detect and respond to cyberattacks, the cyber-power system's capability to withstand and recover from cyberattacks is enhanced in the increasingly technology-dependent power grid environment.

Today, cyber-physical systems (CPSs) are ubiquitous. Whether it is robotics, electric vehicles, the smart home, autonomous driving, or smart prosthetics, CPSs shape our day-to-day lives. Yet, designing and programming CPSs becomes evermore challenging as the overall complexity of systems increases. CPSs need to interface (potentially distributed) computation with concurrent processes in the physical world while fulfilling strict safety requirements. Modern and popular frameworks for designing CPS applications, such as ROS and AUTOSAR, address the complexity challenges by emphasizing scalability and reactivity. This, however, comes at the cost of compromising determinism and the time predictability of applications, which ultimately compromises safety. This thesis argues that this compromise is not a necessity and demonstrates that scalability can be achieved while ensuring a predictable execution. At the core of this thesis is the novel reactor model of computation (MoC) that promises to provide timed semantics, reactivity, scalability, and determinism. A comprehensive study of related models indicates that there is indeed no other MoC that provides similar properties. The main contribution of this thesis is the introduction of a complete set of tools that make the reactor model accessible for CPS design and a demonstration of their ability to facilitate the development of scalable deterministic software. After introducing the reactor model, we discuss its key principles and utility through an adaptation of reactors in the DEAR framework. This framework integrates reactors with a popular runtime for adaptive automotive applications developed by AUTOSAR. An existing AUTOSAR demonstrator application serves as a case study that exposes the problem of nondeterminism in modern CPS frameworks. We show that the reactor model and its implementation in the DEAR framework are applicable for achieving determinism in industrial use cases. Building on the reactor model, we introduce the polyglot coordination language Lingua Franca (LF), which enables the definition of reactor programs independent of a concrete target programming language. Based on the DEAR framework, we develop a full-fledged C++ reactor runtime and a code generation backend for LF. Various use cases studied throughout the thesis illustrate the general applicability of reactors and LF to CPS design, and a comprehensive performance evaluation using an optimized version of the C++ reactor runtime demonstrates the scalability of LF programs. We also discuss some limitations of the current scheduling mechanisms and show how they can be overcome by partitioning programs. Finally, we consider design space exploration (DSE) techniques to further improve the scalability of LF programs and manage hardware complexity by automating the process of allocating hardware resources to specific components in the program. This thesis contributes the Mocasin framework, which resembles a modular platform for prototyping and researching DSE flows. While a concrete integration with LF remains for future work, Mocasin provides a foundation for exploring DSE in Lingua Franca.

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

A key problem in mobile computing is providing people access to necessary cyber-information associated with their surrounding physical objects. Mobile augmented reality is one of the emerging techniques that address this key problem by allowing users to see the cyber-information associated with real-world physical objects by overlaying that cyber-information on the physical objects's imagery. As a consequence, many mobile augmented reality approaches have been proposed to identify and visualize relevant cyber-information on users' mobile devices by intelligently interpreting users' positions and orientations in 3D and their associated surroundings. However, existing approaches for mobile augmented reality primarily rely on Radio Frequency (RF) based location tracking technologies (e.g., Global Positioning Systems or Wireless Local Area Networks), which typically do not provide sufficient precision in RF-denied areas or require additional hardware and custom mobile devices. To remove the dependency on external location tracking technologies, this dissertation presents a new vision-based context-aware approach for mobile augmented reality that allows users to query and access semantically-rich 3D cyber-information related to real-world physical objects and see it precisely overlaid on top of imagery of the associated physical objects. The approach does not require any RF-based location tracking modules, external hardware attachments on the mobile devices, and/or optical/fiducial markers for localizing a user's position. Rather, the user's 3D location and orientation are automatically and purely derived by comparing images from the user's mobile device to a 3D point cloud model generated from a set of pre-collected photographs. A further challenge of mobile augmented reality is creating 3D cyber-information and associating it with real-world physical objects, especially using the limited 2D user interfaces in standard mobile devices. To address this challenge, this research provides a new image-based 3D cyber-physical content authoring method designed specifically for the limited screen sizes and capabilities of commodity mobile devices. This new approach does not only provide a method for creating 3D cyber-information with standard mobile devices, but also provides an automatic association of user-driven cyber-information with real-world physical objects in 3D. Finally, a key challenge of scalability for mobile augmented reality is addressed in this dissertation. In general, mobile augmented reality is required to work regardless of users' location and environment, in terms of physical scale, such as size of objects, and in terms of cyber-information scale, such as total number of cyber-information entities associated with physical objects. However, many existing approaches for mobile augmented reality have mainly tested their approaches on limited real-world use-cases and have challenges in scaling their approaches. By designing fast direct 2D-to-3D matching algorithms for localization, as well as applying caching scheme, the proposed research consistently supports near real-time localization and information association regardless of users' location, size of physical objects, and number of cyber-physical information items. To realize all of these research objectives, five research methods are developed and validated: 1) Hybrid 4-Dimensional Augmented Reality (HD4AR), 2) Plane transformation based 3D cyber-physical content authoring from a single 2D image, 3) Cached k-d tree generation for fast direct 2D-to-3D matching, 4) double-stage matching algorithm with a single indexed k-d tree, and 5) K-means Clustering of 3D physical models with geo-information. After discussing each solution with technical details, the perceived benefits and limitations of the research are discussed with validation results. / Ph. D.

The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. Big data is a widely-used term without a clear definition. The difference between big data and traditional data can be characterized by four Vs: velocity (speed at which data is generated), volume (amount of data generated), variety (the data can take on different forms), and veracity (the data may be of poor/unknown quality). As many industries begin to recognize the value of big data, organizations try to capture it through means such as: side-channel data in a manufacturing operation, unstructured text-data reported by healthcare personnel, various demographic information of households from census surveys, and the range of communication data that define communities and social networks. Big data analytics generally follows this framework: first, a digitized process generates a stream of data, this raw data stream is pre-processed to convert the data into a usable format, the pre-processed data is analyzed using statistical tools. In this stage, called statistical learning of the data, analysts have two main objectives (1) develop a statistical model that captures the behavior of the process from a sample of the data (2) identify anomalies in the process. However, several open challenges still exist in this framework for big data analytics. Recently, data types such as free-text data are also being captured. Although many established processing techniques exist for other data types, free-text data comes from a wide range of individuals and is subject to syntax, grammar, language, and colloquialisms that require substantially different processing approaches. Once the data is processed, open challenges still exist in the statistical learning step of understanding the data. Statistical learning aims to satisfy two objectives, (1) develop a model that highlights general patterns in the data (2) create a signaling mechanism to identify if outliers are present in the data. Statistical modeling is widely utilized as researchers have created a variety of statistical models to explain everyday phenomena such as predicting energy usage behavior, traffic patterns, and stock market behaviors, among others. However, new applications of big data with increasingly varied designs present interesting challenges. Consider the example of free-text analysis posed above. There's a renewed interest in modeling free-text narratives from sources such as online reviews, customer complaints, or patient safety event reports, into intuitive themes or topics. As previously mentioned, documents describing the same phenomena can vary widely in their word usage and structure. Another recent interest area of statistical learning is using the environmental conditions that people live, work, and grow in, to infer their quality of life. It is well established that social factors play a role in overall health outcomes, however, clinical applications of these social determinants of health is a recent and an open problem. These examples are just a few of many examples wherein new applications of big data pose complex challenges requiring thoughtful and inventive approaches to processing, analyzing, and modeling data. Although a large body of research exists in the area of anomaly detection increasingly complicated data sources (such as side-channel related data or network-based data) present equally convoluted challenges. For effective anomaly-detection, analysts define parameters and rules, so that when large collections of raw data are aggregated, pieces of data that do not conform are easily noticed and flagged. In this work, I investigate the different steps of the data analytics framework and propose improvements for each step, paired with practical applications, to demonstrate the efficacy of my methods. This paper focuses on the healthcare, manufacturing and social-networking industries, but the materials are broad enough to have wide applications across data analytics generally. My main contributions can be summarized as follows: • In the big data analytics framework, raw data initially goes through a pre-processing step. Although many pre-processing techniques exist, there are several challenges in pre-processing text data and I develop a pre-processing tool for text data. • In the next step of the data analytics framework, there are challenges in both statistical modeling and anomaly detection o I address the research area of statistical modeling in two ways: - There are open challenges in defining models to characterize text data. I introduce a community extraction model that autonomously aggregates text documents into intuitive communities/groups - In health care, it is well established that social factors play a role in overall health outcomes however developing a statistical model that characterizes these relationships is an open research area. I developed statistical models for generalizing relationships between social determinants of health of a cohort and general medical risk factors o I address the research area of anomaly detection in two ways: - A variety of anomaly detection techniques exist already, however, some of these methods lack a rigorous statistical investigation thereby making them ineffective to a practitioner. I identify critical shortcomings to a proposed network based anomaly detection technique and introduce methodological improvements - Manufacturing enterprises which are now more connected than ever are vulnerably to anomalies in the form of cyber-physical attacks. I developed a sensor-based side-channel technique for anomaly detection in a manufacturing process / PHD / The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. The fields of manufacturing and healthcare are two examples of industries that are currently undergoing significant transformations due to the rise of big data. The addition of large sensory systems is changing how parts are being manufactured and inspected and the prevalence of Health Information Technology (HIT) systems in healthcare systems is also changing the way healthcare services are delivered. These industries are turning to big data analytics in the hopes of acquiring many of the benefits other sectors are experiencing, including reducing cost, improving safety, and boosting productivity. However, there are many challenges that exist along with the framework of big data analytics, from pre-processing raw data, to statistical modeling of the data, and identifying anomalies present in the data or process. This work offers significant contributions in each of the aforementioned areas and includes practical real-world applications. Big data analytics generally follows this framework: first, a digitized process generates a stream of data, this raw data stream is pre-processed to convert the data into a usable format, the pre-processed data is analyzed using statistical tools. In this stage, called ‘statistical learning of the data’, analysts have two main objectives (1) develop a statistical model that captures the behavior of the process from a sample of the data (2) identify anomalies or outliers in the process. In this work, I investigate the different steps of the data analytics framework and propose improvements for each step, paired with practical applications, to demonstrate the efficacy of my methods. This work focuses on the healthcare and manufacturing industries, but the materials are broad enough to have wide applications across data analytics generally. My main contributions can be summarized as follows: • In the big data analytics framework, raw data initially goes through a pre-processing step. Although many pre-processing techniques exist, there are several challenges in pre-processing text data and I develop a pre-processing tool for text data. • In the next step of the data analytics framework, there are challenges in both statistical modeling and anomaly detection o I address the research area of statistical modeling in two ways: - There are open challenges in defining models to characterize text data. I introduce a community extraction model that autonomously aggregates text documents into intuitive communities/groups - In health care, it is well established that social factors play a role in overall health outcomes however developing a statistical model that characterizes these relationships is an open research area. I developed statistical models for generalizing relationships between social determinants of health of a cohort and general medical risk factors o I address the research area of anomaly detection in two ways: - A variety of anomaly detection techniques exist already, however, some of these methods lack a rigorous statistical investigation thereby making them ineffective to a practitioner. I identify critical shortcomings to a proposed network-based anomaly detection technique and introduce methodological improvements - Manufacturing enterprises which are now more connected than ever are vulnerable to anomalies in the form of cyber-physical attacks. I developed a sensor-based side-channel technique for anomaly detection in a manufacturing process.

Artificial Intelligence is a fuzzy concept. My role, as I see it, is to put down a working definition, a criterion, and a set of assumptions to set up equations for a workable methodology. This research introduces the notion of Artificial Intelligent Agency, denoting the application of Artificial General Intelligence. The problem being handled by mathematics and logic, and only thereafter semantics, is Self-Supervised Machine Learning (SSML) towards Intuitive Vehicle Health Management, in the domain of cybernetic-physical science. The present work stems from a broader engagement with a major multinational automotive OEM, where Intelligent Vehicle Health Management will dynamically choose suitable variants only to realise predefined variation points. Physics-based models infer properties of a model of the system, not properties of the implemented system itself. The validity of their inference depends on the models’ degree of fidelity, which is always an approximate localised engineering abstraction. In sum, people are not very good at establishing causality. To deduce new truths from implicit patterns in the data about the physical processes that generate the data, the kernel of this transformative technology is the intersystem architecture, occurring in-between and involving the physical and engineered system and the construct thereof, through the communication core at their interface. In this thesis it is shown that the most practicable way to establish causality is by transforming application models into actual implementation. The hypothesis being that the ideal source of training data for SSML, is an isomorphic monoid of indexical facts, trace-preserving events of natural kind.

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.