We are living in the age of information technology, where we are fully occupied with the revolutionary innovations of the last few decades such as the Internet, mobile devices, wireless communications, social networks, wearables, cloud computing, etc. While these technologies have become integral part of our daily life, we are now anxiously waiting to embrace Internet-of-Things (IoT), intelligent digital assistants, driver-less cars, drone deliveries, virtual reality, and smart city applications. Recently, research community is demonstrating increasing interests about Cyber-Physical Systems (CPS) that resides in the cross-section of embedded systems, network communications, and scalable distributed infrastructures. The main responsibility of a CPS is to collect sensory data about the physical world and to inform the computation module using communication technologies that processes the data, identifies important insights and notifies back using a feedback loop. These notifications can however be control commands to reconfigure the physical world. Such a setup is a useful method to deploy smart city applications. In this dissertation, we keep our focus onto the smart transport objective using vehicular CPS (VCPS) based systems organization. We have compiled this dissertation with our research contributions in this growing field of VCPS. One of our key contributions in this field is an architecture reference model for the cloud-based CPS, C2PS, where we analytically describe the key properties of a CPS: computation, communication and control, while integrating cloud features to it. We have identified various types of computation and interaction modes of this paradigm as well as describe Bayesian network and fuzzy logic based smart connection to select a mode at any time. It is considered that the true adoption of CPS is only possible through the deployment of the IoT systems. Thus, it is important to have IoT as a foundation in the CPS architectures. Our next contribution is to leverage existing Vehicular Adhoc Network (VANET) technologies and map them with the standard IoT-Architecture reference model to design the VCPS, Social Internet-of-Vehicles (SIoV). In this process, we have identified the social structures and system interactions among the subsystems involved in the SIoV. We also present a message structure to facilitate different types of SIoV interactions. The ability of dynamic reconfiguration in a C2PS is very appealing. We capture this feature in the VCPS by designing a model-based reconfiguration scheme for the SIoV, where we measure the data workloads of distinct subsystems involved in various types of SIoV interactions. We further use these models to design dynamic adaptation schemes for the subsystems involved in VCPS interactions. Our final contribution is an application development platform based on C2PS design technique that uses server-client based system communications. In this platform, server side is built using JAVA, client side uses Android, message communication uses JSON and every component has its own MySQL database to store the interactions. We use this platform to emulate and deploy SIoV related applications and scenarios. Such a platform is necessary to continue C2PS related research and developments in the laboratory environment.

The growing adoption of smart manufacturing systems and its related technologies (e.g., embedded sensing, internet-of-things, cyber-physical systems, big data analytics, and cloud computing) is promising a paradigm shift in the manufacturing industry. Such systems enable extracting and exchanging actionable knowledge across the different entities of the manufacturing cyber-physical system and beyond. From a quality control perspective, this allows for more opportunities to realize proactive product design; real-time process monitoring, diagnosis, prognosis, and control; and better product quality characterization. However, a multitude of challenges are arising, with the growing adoption of smart manufacturing, including industrial data characterized by increasing volume, velocity, variety, and veracity, as well as the security of the manufacturing system in the presence of growing connectivity. Taking advantage of these emerging opportunities and tackling the upcoming challenges require creating novel quality control and data analytics methods, which not only push the boundaries of the current state-of-the-art research, but discover new ways to analyze the data and utilize it. One of the key pillars of smart manufacturing systems is real-time automated process monitoring, diagnosis, and control methods for process/product anomalies. For machining applications, traditionally, deterioration in quality measures may occur due to a variety of assignable causes of variation such as poor cutting tool replacement decisions and inappropriate choice cutting parameters. Additionally, due to increased connectivity in modern manufacturing systems, process/product anomalies intentionally induced through malicious cyber-attacks -- aiming at degrading the process performance and/or the part quality -- is becoming a growing concern in the manufacturing industry. Current methods for detecting and diagnosing traditional causes of anomalies are primarily lab-based and require experts to perform initial set-ups and continual fine-tuning, reducing the applicability in industrial shop-floor applications. As for efforts accounting for process/product anomalies due cyber-attacks, these efforts are in early stages. Therefore, more foundational research is needed to develop a clear understanding of this new type of cyber-attacks and their effects on machining processes, to ensure smart manufacturing security both on the cyber and the physical levels. With primary focus on machining processes, the overarching goal of this dissertation work is to explore new ways to expand the use and value of manufacturing data-driven methods for better applicability in industrial shop-floors and increased security of smart manufacturing systems. As a first step toward achieving this goal, the work in this dissertation focuses on adopting this goal in three distinct areas of interest: (1) Statistical Process Monitoring of Time-Between-Events Data (e.g., failure-time data); (2) Defending against Product-Oriented Cyber-Physical Attacks on Intelligent Machining Systems; and (3) Modeling Machining Process Data: Time Series vs. Spatial Point Cloud Data Structures. / PHD / Recent advancements in embedded sensing, internet-of-things, big data analytics, cloud computing, and communication technologies and methodologies are shifting the modern manufacturing industry toward a novel operational paradigm. Several terms have been coined to refer to this new paradigm such as cybermanufacturing, industry 4.0, industrial internet of things, industrial internet, or more generically smart manufacturing (term to be used henceforth). The overarching goal of smart manufacturing is to transform modern manufacturing systems to knowledge-enabled Cyber-Physical Systems (CPS), in which humans, machines, equipment, and products communicate and cooperate together in real-time, to make decentralized decisions resulting in profound improvements in the entire manufacturing ecosystem. From a quality control perspective, this allows for more opportunities to utilize manufacturing process data to realize proactive product design; real-time process monitoring, diagnosis, prognosis, and control; and better product quality characterization. With primary focus on machining processes, the overarching goal of this work is to explore new ways to expand the use and value of manufacturing data-driven methods for better applicability in industrial shop-floors and increased security of smart manufacturing systems. As a first step toward achieving this goal, the work in this dissertation focuses on three distinct areas of interest: (1) Monitoring of time-between-events data of mechanical components replacements (e.g., failure-time data); (2) Defending against cyber-physical attacks on intelligent machining systems aiming at degrading machined parts quality; and (3) Modeling machining process data using two distinct data structures, namely, time series and spatial point cloud data.

With recent advancements in computer and network technologies, cyber-physical systems have become more susceptible to cyber-attacks; and production systems are no exception. Unlike traditional Information Technology (IT) systems, cyber-physical systems are not limited to attacks aimed at Intellectual Property (IP) theft, but also include attacks that maliciously affect the physical world. In manufacturing, such cyber-physical attacks can destroy equipment, force dimensional product changes, alter a product's mechanical characteristics, or endanger human lives. The manufacturing industry often relies on modern Quality Control (QC) tools to protect against quality losses, such as those that can occur from an attack. However, cyber-physical attacks can still be designed to avoid detection by traditional QC methods, which suggests a strong need for new and more robust QC tools. Such new tools should be able to prevent, or at least minimize, the effects of cyber-physical attacks on production systems. Unfortunately, little to no research has been done on using QC tools for cyber-physical security of production systems. Hence, the overarching goal of this work is to allow QC systems to be designed and used effectively as a second line of defense, when traditional cyber-security techniques fail and the production system is already breached. To this end, this work focuses on: 1) understanding the role of QC systems in cyber-physical attacks within manufacturing through developing a taxonomy encompassing the different layers involved; 2) identifying existing weaknesses in QC tools and exploring the effects of exploiting them by cyber-physical attacks; and 3) proposing more effective QC tools that can overcome existing weaknesses by introducing randomness to the tools, for better security against cyber-physical attacks in manufacturing. / Ph. D. / The recent technological developments in computers and networking have made systems, such as production systems, more vulnerable to attacks having both cyber and physical components; i.e., to cyber-physical attacks. In manufacturing, such attacks are not only capable of stealing valuable information, but can also destroy equipment, force physical product changes, alter product’s mechanical characteristics, or endanger human lives. Typically, the manufacturing industry have relied on various Quality Control (QC) tools, such as product inspection, to detect the effects caused by these attacks. However, these attacks could be still designed in a way to avoid detection by traditional QC methods, which suggests a need for new and more effective QC tools. Such new tools should be able to prevent, or at least minimize, the effects of these attacks in manufacturing. Unfortunately, almost no research has been done on using QC tools for securing production systems against these malicious attacks. Hence, the overarching goal of this work is to allow QC systems to be designed in a more effective manner to act as a second line of defense, when traditional cyber-security measures and attackers have already accessed the production system. To this end, this work focuses on: 1) understanding the role of QC systems during the attack; 2) identifying existing weaknesses in QC tools and determining the effects of exploiting them by the attack; and 3) proposing more effective QC tools, for better protection against these types of cyber-physical attacks in manufacturing.

Master of Science / Department of Computer Science / Eugene Vasserman / There is continuing growth in the need to secure critical infrastructures from malicious adversaries. These adversaries can attack systems from different forms. They can physically break in and steal something important, or they can attack from the cyber realm in order to steal critical information. This project combines the modeling process for physical spaces along with a logic reasoning tool that can identify the state of a networked device in order to analyze large enterprise systems for combined cyber-physical vulnerabilities. Using a pure model checker would not be able to handle the near infinite states that a computer or networked device may be in. Therefore this new approach combines the use of a logic analyzer tool that with a well-defined set of rules that reasons about the security and trustworthiness of devices in the model. While there has been long study of how to secure a building from intrusion, and much research about defense against cyber attacks, there is always a large gap between the two in practice. This approach may no longer be sufficient against today’s adversaries and offers little to no defense against insider threats. Combining the two in this new form allows for a more complete security view and protection against more advanced adversaries. Then this thesis shows how this approach meets a series of requirements for an effective vulnerability analysis. This is achieved by executing a model based on a real world facility with a series of induced faults that would on their own not be enough to be a vulnerability but tied together would have series consequences. This thesis shows how this approach can then be used to detail potentially unseen vulnerabilities and develop fixes for them to help create a more secure facility.

The objective of this thesis is to understand the interactions between the computational mechanisms, described by algorithms and software, and the physical world, described by differential equations, in the context of networked systems. Such systems can be denoted as cyber-physical nodes connected over a network. In this work, the power grid is used as a guiding example and a rich source of problems which can be generalized to networked cyber-physical systems. We address specific problems that arise in cyber-physical networks due to the presence of a computational network and a physical network as well as provide directions for future research.

Smartness of technical systems relies also on appropriate actuators and sensors. Different to the prevalent definition of smartness to be embedded machine intelligence, in this paper elegance and simplicity of solutions is postulated be a more uniform and useful characterization. This is discussed in view of the current trends towards cyber physical systems and the role of components and subsystems, as well as of models for their effective realization. Current research on actuators and sensing in the fluid power area has some emphasis on simplicity and elegance of solution concepts and sophisticated modeling. This is demonstrated by examples from sensorless positioning, valve actuation, and compact hydraulic power supply.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Balasubramaniam Natarajan / Caterina Scoglio / Many physical and engineered systems (e.g., smart grid, transportation and biomedical systems) are increasingly being monitored and controlled over a communication network. These systems where sensing, communication, computation and real time control are closely integrated are referred to as cyber physical systems (CPS). Cyber physical systems present a plethora of challenges related to their design, analysis, optimization and control. In this dissertation, we present some fundamental methodologies to analyze the optimization of physical systems over a communication network. Specifically, we consider a medium voltage DC shipboard smart grid (SSG) reconfiguration problem as a test case to demonstrate our approach. The main goal of SSG reconfiguration is to change the topology of the physical power system by switching circuit breakers, switches, and other devices in the system in order to route power effectively to loads especially in the event of faults/failures. A majority of the prior work has focused on centralized approaches to optimize the switch configuration to maximize specific objectives. These methods are prohibitively complex and not suited for agile reconfiguration in mission critical situations. Decentralized solutions proposed do reduce complexity and implementation time at the cost of optimality. Unfortunately, none of the prior efforts in this arena address the cyber physical aspects of an SSG. This dissertation aims to bridge this gap by proposing a suite of methods to analyze both centralized and decentralized SSG reconfigurations that incorporate the effect of the underlying cyber infrastructure. The SSG reconfiguration problem is a mixed integer non convex optimization problem for which branch and bound based solutions have been proposed earlier. Here, optimal reconfiguration strategies prioritize the power delivered to vital loads over semi-vital and non vital loads. In this work, we propose a convex approximation to the original non convex problem that significantly reduces complexity of the SSG reconfiguration. Tradeoff between power delivered and number of switching operations after reconfiguration is discussed at steady state. Second, the distribution of end-to-end delay associated with fault diagnosis and reconfiguration in SSG is investigated from a cyber-physical system perspective. Specifically, a cross-layer total (end-to-end) delay analysis framework is introduced for SSG reconfiguration. The proposed framework stochastically models the heterogeneity of actions of various sub-systems viz., the reconfiguration of power systems, generation of fault information by sensor nodes associated to the power system, processing actions at control center to resolve fault locations and reconfiguration, and information flow through communication network to:(1) analyze the distribution of total delay in SSG reconfiguration after the occurrence of faults; and (2) propose design options for real-time reconfiguration solutions for shipboard CPS, that meet total delay requirements. Finally, the dissertation focuses on the quality of SSG reconfiguration solution with incomplete knowledge of the overall system state, and communication costs that may affect the quality (optimality) of the resulting reconfiguration. A dual decomposition based decentralized optimization in which the shipboard system is decomposed into multiple separable subsystems with agents is proposed. Specifically, agents monitoring each subsystem solve a local concave dual function of the original objective while neighboring agents share information over a communication network to obtain a global solution. The convergence of the proposed approach under varying network delays and quantization noise is analyzed and comparisons with centralized approaches are presented. Results demonstrate the effectiveness as well as tradeoffs involved in centralized and decentralized SSG reconfiguration approaches.

Automated vehicles are getting closer each day to large-scale deployment. It is expected that self-driving cars will be able to alleviate traffic congestion by safely operating at distances closer than human drivers are capable of and will overall improve traffic throughput. In these conditions, passenger safety and security is of utmost importance. When multiple autonomous cars follow each other on a highway, they will form what is known as a cyber-physical system. In a general setting, there are tools to assess the level of influence a possible attacker can have on such a system, which then describes the level of safety and security. An attacker might attempt to counter the benefits of automation by causing collisions and/or decreasing highway throughput. These strings (platoons) of automated vehicles will rely on control algorithms to maintain required distances from other cars and objects around them. The vehicle dynamics themselves and the controllers used will form the cyber-physical system and its response to an attacker can be assessed in the context of multiple interacting vehicles. While the vehicle dynamics play a pivotal role in the security of this system, the choice of controller can also be leveraged to enhance the safety of such a system. After knowledge of some attacker capabilities, adversarial-aware controllers can be designed to react to the presence of an attacker, adding an extra level of security. This work will attempt to address these issues in vehicular platooning. Firstly, a general analysis concerning the capabilities of possible attacks in terms of control system theory will be presented. Secondly, mitigation strategies to some of these attacks will be discussed. Finally, the results of an experimental validation of these mitigation strategies and their implications will be shown.

This dissertation treats a variety of topics in the computer security domain which have direct impact on everyday life. The first extends false data injection attacks against state estimation in electric power grids and then provides a novel power flow model camouflage method to hamper these attacks. The second deals with automotive theft response, detailing a method for a car to intelligently identify when it has been stolen, based on collected behavioral traits of its driver. The third demonstrates a new attack against the content integrity of the PDF file format, caus- ing humans and computers to see different information within the same PDF documents. This dissertation lastly describes some future work efforts, identifying some potential vulnerabilities in the automated enforcement of copyright protection for audio (particularly music) in online systems such as YouTube.

The growing connectivity of cyber-physical systems (CPSes) has led to an increased concern over the ability of cyber-attacks to inflict physical damage. Current cybersecurity measures focus on preventing attacks from penetrating control supervisory networks. These reactive techniques, however, are often plagued with vulnerabilities and zero-day exploits. Embedded processors in CPS field devices often possess little security of their own, and are easily exploited once the network is penetrated. In response, researchers at Georgia Tech and Virginia Tech have proposed a Trustworthy Autonomic Interface Guardian Architecture (TAIGA), which monitors communication between the embedded controller and physical process. This autonomic architecture provides the physical process with a last line of defense against cyber-attacks by switching process control to a trusted backup controller if an attack causes a system specification violation. This thesis focuses on classifying the effects of cyberattacks on embedded controllers, evaluating TAIGA’s resilience against these attacks, and determining the applicability of TAIGA to other CPSes. This thesis identifies four possible outcomes of a cyber-attack on a CPS embedded processor. We then evaluate TAIGA’s mechanisms to defend against those attack outcomes, and verify TAIGA satisfies the listed trust requirements. Next, we discuss an implementation and the experimental results of TAIGA on a hazardous cargo transportation robot. Then, by making various modifications to the setup configuration, we are able to explore TAIGA’s ability to provide security and process protection to other CPSes with varying levels of autonomy or distributed components.