Cyber-physical systems (CPS) are systems that are composed of physical and computational components. CPS components are typically interconnected through a communication network that allows components to interact and take automated actions that are beneficial for the overall CPS. Future Power-Grid is one of the major example of Cyber-physical systems. Traditionally, Power-Grids use a centralized approach to manage the energy produced at power sources or large power plants. Due to the advancement and availability of renewable energy sources such as wind farms and solar systems, there are more number of energy sources connecting to the power grid. Managing these large number of energy sources using a centralized technique is not practical and is computationally very expensive. Therefore, a decentralized way of monitoring and scheduling of energy across the power grid is preferred. In a decentralized approach, computational load is distributed among the grid entities that are interconnected through a readily available communication network like internet. The communication network allows the grid entities to coordinate and exchange their power state information with each other and take automated actions that lead to efficient consumption of energy as well as the network bandwidth. Thus, the future power grid is appropriately called a "Smart-Grid". While Smart-Grids provide efficient energy operations, they also impose several challenges in the design, verification and monitoring phases. The computer network serves as a backbone for scheduling messages between the Smart-Grid entities. Therefore, network delays experienced by messages play a vital role in grid stability and overall system performance. In this work, we study the effects of network delays on Smart-Grid performance and propose adaptive algorithms to efficiently schedule messages between the grid entities. Algorithms proposed in this work also ensure the grid stability and perform network congestion control. Through this work, we derive useful conclusions regarding the Smart-Grid performance and find new challenges that can serve as future research directions in this domain.

Ces travaux se focalisent sur une problématique fondamentale de la robotique autonome: l'estimation d'état. En effet, la plupart des approches actuelles permettant à un robot autonome de réaliser une tâche requièrent tout d'abord l'extraction d'une information d'état à partir de mesures capteurs bruitées. Ce vecteur d'état contient un ensemble de variables caractérisant le système à un instant t, comme la position du robot, sa vitesse, etc. En robotique comme dans de nombreux autres domaines, le filtrage bayésien est devenu la solution la plus populaire pour estimer l'état d'un système de façon robuste et à haute fréquence. Le succès du filtrage bayésien réside dans sa relative simplicité, que ce soit dans la mise en oeuvre des équations récursives de filtrage, ou encore dans la représentation simplifiée et intuitive du système au travers du modèle de Markov caché d'ordre 1. Généralement, un filtre bayésien repose sur une description minimaliste de l'état du système. Cette représentation simplifiée permet de conserver un temps d'exécution réduit, mais est également la conséquence de notre compréhension partielle du fonctionnement du système physique. Tous les aspects inconnus ou non modélisés du système sont ensuite représentés de façon globale par l'adjonction de composantes de bruit. Si ces composantes de bruit constituent une représentation simple et unifiée des aspects non modélisés du système, il reste néanmoins difficile de trouver des paramètres de bruit qui sont pertinents dans tous les contextes. En effet, à l'opposé de ce principe de modélisation, la problématique de navigation autonome pose le problème de la multiplicité d'environnements différents pour lesquels il est nécessaire de s'adapter intelligemment. Cette problématique nous amène donc à réviser la modélisation des aspects inconnus du systèmes sous forme de bruits stationnaires, et requiert l'introduction d'une information de contexte au sein du processus de filtrage. Dans ce cadre, ces travaux se focalisent spécifiquement sur l'amélioration du modèle état-observation sous-jacent au filtre bayésien afin de le doter de capacités d'adaptation vis-à-vis des perturbations contextuelles modifiant les performances capteurs. L'objectif principal est donc ici de trouver l'équilibre entre complexité du modèle et modélisation précise des phénomènes physiques représentés au travers d'une information de contexte. Nous établissons cet équilibre en modifiant le modèle état-observation afin de compenser les hypothèses simplistes de bruit stationnaire tout en continuant de bénéficier du faible temps de calcul requis par les équations récursives. Dans un premier temps, nous définissons une information de contexte basée sur un ensemble de mesures capteurs brutes, sans chercher à identifier précisément la typologie réelle de contextes de navigation. Toujours au sein du formalisme bayésien, nous exploitons des méthodes d'apprentissage statistique pour identifier une distribution d'observation non stationnaire et dépendante du contexte. cette distribution repose sur l'introduction de deux nouvelles composantes: un modèle destiné à prédire le bruit d'observation pour chaque capteur, et un modèle permettant de sélectionner un sous-ensemble de mesures à chaque itération du filtre. Nos investigations concernent également l'impact des méthodes d'apprentissage: dans le contexte historique du filtrage bayésien, le modèle état-observation est traditionnellement appris de manière générative, c'est à dire de manière à expliquer au mieux les paires état-observation contenues dans les données d'apprentissage. Cette méthode est ici remise en cause puisque, bien que fondamentalement génératif, le modèle état-observation est uniquement exploité au travers des équations de filtrage, et ses capacités génératives ne sont donc jamais utilisées[...] / Prevalent approaches for endowing robots with autonomous navigation capabilities require the estimation of a system state representation based on sensor noisy information. This system state usually depicts a set of dynamic variables such as the position, velocity and orientation required for the robot to achieve a task. In robotics, and in many other contexts, research efforts on state estimation converged towards the popular Bayes filter. The primary reason for the success of Bayes filtering is its simplicity, from the mathematical tools required by the recursive filtering equations, to the light and intuitive system representation provided by the underlying Hidden Markov Model. Recursive filtering also provides the most common and reliable method for real-time state estimation thanks to its computational efficiency. To keep low computational complexity, but also because real physical systems are not perfectly understood, and hence never faithfully represented by a model, Bayes filters usually rely on a minimum system state representation. Any unmodeled or unknown aspect of the system is then encompassed within additional noise terms. On the other hand, autonomous navigation requires robustness and adaptation capabilities regarding changing environments. This creates the need for introducing contextual awareness within the filtering process. In this thesis, we specifically focus on enhancing state estimation models for dealing with context-dependent sensor performance alterations. The issue is then to establish a practical balance between computational complexity and realistic modelling of the system through the introduction of contextual information. We investigate on achieving this balance by extending the classical Bayes filter in order to compensate for the optimistic assumptions made by modeling the system through time-homogeneous distributions, while still benefiting from the recursive filtering computational efficiency. Based on raw data provided by a set of sensors and any relevant information, we start by introducing a new context variable, while never trying to characterize a concrete context typology. Within the Bayesian framework, machine learning techniques are then used in order to automatically define a context-dependent time-heterogeneous observation distribution by introducing two additional models: a model providing observation noise predictions and a model providing observation selection rules.The investigation also concerns the impact of the training method we choose. In the context of Bayesian filtering, the model we exploit is usually trained in the generative manner. Thus, optimal parameters are those that allow the model to explain at best the data observed in the training set. On the other hand, discriminative training can implicitly help in compensating for mismodeled aspects of the system, by optimizing the model parameters with respect to the ultimate system performance, the estimate accuracy. Going deeper in the discussion, we also analyse how the training method changes the meaning of the model, and how we can properly exploit this property. Throughout the manuscript, results obtained with simulated and representative real data are presented and analysed.

Cyber-physical systems (CPS) refer to a group of systems that combine the real physical world with cyber components. Traditionally, the applications of CPS in research and the real world mainly include smart power grid, autonomous automobile systems, and robotics systems. In recent years, due to the fast development of pervasive computing, sensor manufacturing, and artificial intelligence technologies, mobile cyber-physical systems that extend the application domains of traditional cyber-physical systems have become increasingly popular. In mobile cyber-physical systems, devices have rich features, such as significant computational resources, multiple communication radios, various sensor modules, and high-level programming languages. These features enable us to build more powerful and convenient applications and systems for mobile users. At the same time, such information can also be leveraged by attackers to design new types of attacks. The security and privacy issues can exist in any application of mobile CPS. In terms of defense systems, we focus on three important topics: voice liveness detection, face forgery detection, and securing PIN-based authentication. In terms of attack systems, we study the location privacy in augmented reality (AR) applications. We first investigate the voice replay attacks on smartphones. Voice input is becoming an important interface on smartphones since it can provide better user experience compared with traditional typing-based input methods. However, because the human voice is often exposed to the public, attackers can easily steal victims' voices and replay it to victims' devices to issue malicious commands. To defend the smartphone from voice replay attacks, we propose a novel liveness detection system, which can determine whether the incoming voice is from a live person or a loudspeaker. The key idea is that voices are produced and finalized at multiple positions in human vocal systems, while the audio signals from loudspeakers are from one position. By using two microphones on the smartphone to record the voice at two positions and measure their relationship, the proposed system can defend against voice replay attacks with a high success rate. Besides smartphones, voice replay attacks are also feasible on AR headsets. However, due to the special hardware positions, the current voice liveness detection system designed for smartphones cannot be deployed on AR headsets. To address this issue, we propose a novel voice liveness detection system for AR headsets. The key insight is that the human voice can propagate through the internal body. By attaching a contact microphone around the user's temple, we can collect the internal body voice. A voice is determined from a live person as long as the collected internal body voice has a strong relationship with the mouth voice. Since the contact microphone is cheap, tiny, and thin, it can be embedded in current AR headsets with minimal additional cost. Next, we propose a system to detect the fake face in real-time video chat. Recent developments in deep learning-based forgery techniques largely improved the ability of forgery attackers. With the help of face reenactment techniques, attackers can transfer their facial expressions to another person's face to create fake facial videos in real-time with very high quality. In our system, we find that the face of a live person can reflect the screen light, and this reflected light can be captured by the web camera. Moreover, current face forgery techniques cannot generate such light change with acceptable quality. Therefore, we can measure the correlation and similarity of the luminance changes between the screen light and the face-reflected light to detect the liveness of the face. We also study to leverage IoT devices to enhance the privacy of some daily operations. We find that the widely used personal identification number (PIN) is not secure and can be attacked in many ways. In some scenarios, it is hard to prevent attackers from obtaining the victim's PIN. Therefore, we propose a novel system to secure the PIN input procedure even if the victim's PIN has been leaked. The basic idea is that different people have different PIN input behavior even for the same PIN. Even though attackers can monitor the victim's PIN input behaviors and imitate it afterward, the biological differences among each person's hands still exist and can be used to differentiate them. To capture both PIN input behavior and the biological features, we install a tiny light sensor at the center of the PIN pad to transfer the information into a light signal. By extracting useful features from multiple domains, we can determine whether the PIN input is from the same person with high accuracy. Besides designing new defense systems, we also show that sensory data and side-channel information can be leveraged to launch new types of attacks. We conduct a study on the network traffic of location-based AR applications. We find that it is feasible to infer the real-time location of a user using the short-time network traffic if the downloading jobs are related to the current location. By carefully deploying fake AR contents at some locations, our attack system can infer the location of the user with high accuracy by processing noisy network traffic data. / Computer and Information Science

Cyber-physical systems refer to a new generation of synergy systems with integrated computational and physical processes which interact with one other. The development and simulation of cyber-physical systems (CPSs) are obstructed by the complexity of the subsystems of which they are comprised, fundamental differences in the operation of cyber and physical elements, significant correlative dependencies among the elements, and operation in dynamic and open environments. The Multiple Belief-Desire-Intention (BDI) agent system (BDI multi-agent system) is a promising choice for overcoming these challenges, since it offers a natural way to decompose complex systems or large scale problems into decentralized, autonomous, interacting, more or less intelligent entities. In particular, BDI agents have the ability to interact with, and expand the capabilities of, the physical world through computation, communication, and control. A BDI agent has its philosophical grounds on intentionality and practical reasoning, and it is natural to combine a philosophical model of human practical reasoning with the physical operation and any cyber infrastructure. In this thesis, we introduce the BDI Model, discuss implementations of BDI agents from an ideal theoretical perspective as well as from a more practical perspective, and show how they can be used to bridge the cyber infrastructure and the physical operation using the framework. We then strengthen the framework's performance using the state-of-the-art parallel computing architecture and eventually propose a BDI agent based software framework to enable the efficient modeling and simulation of heterogeneous CPS systems in an integrated manner. / Electrical and Computer Engineering

The power grid has evolved over the course of many decades with the usage of cyber systems and communications such as Supervisory Control And Data Acquisition (SCADA); however, due to their connectivity to the internet, the cyber-power system can be infiltrated by malicious attackers. Encryption is not a singular solution. Currently, there are several cyber security measures in development, including those based on artificial intelligence. However, there is a need for a varying but consistent attack algorithm to serve as a testbed for these AI or other practices to be trained and tested. This is important because in the event of a real attacker, it is not possible to know exactly where they will attack and in what order. Therefore, the proposed method in this thesis is to use criminology concepts and fuzzy logic inference to create this algorithm and determine its effectiveness in making decisions on a cyber-physical system model. The method takes various characteristics of the attacker as an input, builds their ideal target node, and then compares the nodes to the high-impact target and chooses one as the goal. Based on that target and their knowledge, the attackers will attack nodes if they have resources. The results show that the proposed method can be used to create a variety of attacks with varying damaging effects, and one other set of tests shows the possibility for multiple attacks, such as denial of service and false data injection. The proposed method has been validated using an extended cyber-physical IEEE 13-node distribution system and sensitivity tests to ensure that the ruleset created would take each of the inputs well. / Master of Science / For the last decades, information and communications technology has become more commonplace for electric power and energy systems around the world. As a result, it has attracted hackers to take advantage of the cyber vulnerabilities to attack critical systems and cause damage, e.g., the critical infrastructure for electric energy. The power grid is a wide-area, distributed infrastructure with numerous power plants, substations, transmission and distribution lines as well as customer facilities. For operation and control, the power grid needs to acquire measurements from substations and send control commands from the control center to substations. The cyber-physical system has its vulnerabilities that can be deployed by hackers to launch falsified measurements or commands. Much research is concerned with how to detect and mitigate cyber threats. These methods are used to determine if an attack is occurring, and, if so, what to do about it. However, for these techniques to work properly, there must be a way to test how the defense will understand the purpose and target of an actual attack, which is where the proposed modeling and simulation method for an attacker comes in. Using a set of values for their resources, motivation and other characteristics, the defense algorithm determines what the attacker's best target would be, and then finds the closest point on the power grid that they can attack. While there are still resources remaining based on the initial value, the attacker will keep choosing places and then execute the attack. From the results, these input characteristic values for the attacker can affect the decisions the attacker makes, and the damage to the system is reflected by the values too. This is tested by looking at the results for the high-impact nodes for each input value, and seeing what came out of it. This shows that it is possible to model an attacker for testing purposes on a simulation.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Balasubramaniam Natarajan / A cyber physical system (CPS) is an intelligent integration of computation and communication infrastructure for monitoring and/or control of an underlying physical system. In this dissertation, we consider a specific class of CPS architectures where state of the system is spatially distributed in physical space. Examples that fit this category of CPS include, smart distribution gird, smart highway/transportation network etc. We study state estimation and control process in such systems where, (1) multiple sensors and actuators are arbitrarily deployed to jointly sense and control the system; (2) sensors directly communicate their observations to a central estimation and control unit (ECU) over communication links; and, (3) the ECU, on computing the control action, communicates control actions to actuators over communication links. Since communication links are susceptible to random failures, the overall estimation and control process is subjected to: (1) partial observation updates in estimation process; and (2) partial actuator actions in control process. We analyze stochastic stability of estimation and control process, in this scenario by establishing the conditions under which estimation accuracy and deviation from desired state trajectory is bounded. Our key contribution is the derivation of a new fundamental result on bounds for critical probabilities of individual communication link failure to maintain stability of overall system. The overall analysis illustrates that there is trade-off between stability of estimation and control process and quality of underlying communication network. In order to demonstrate practical implication of our work, we also present a case study in smart distribution grid as a system example of spatially distributed CPSs. Voltage/VAR support via distributed generators is studied in a stochastic nonlinear control framework.

Road transports face increasing societal challenges with respect to emissions, safety, and traffic congestion, as well as business challenges. Truck automation, e.g. self-driving trucks may be utilized to address some of these issues. Autonomous transport vehicles may be characterized as Cyber-Physical Systems (CPS). A drawback is that CPS significantly increase technical complexity and thus introduce new challenges to system architecting. A product architecture is the interrelation between physical components and their function, i.e. their purpose. Product architectures can be categorized as being modular or integral. The main purpose of a modular architecture is to enable external variety and at the same time internal commonality. Products with a modular architecture are configured from predesigned building blocks, i.e. modules. A stable module, which is a carrier of main function(s) has standardized interfaces, is configured for company-specific reasons, which means it supports a company-specific (business) strategy. In this thesis, the present state at the heavy vehicle manufacturer Scania, concerning product architecting, modularization, product description and configuration is investigated. Moreover, a new clustering based method for product modularization that integrates product complexity and company business strategies is proposed. The method is logically verified with multiple industrial cases, where the architecture of a heavy truck driveline is used as a test bench. The driveline contains synergistic configurations of mechanical, electrical and software technologies that are constituents of an automated  and/or semi-autonomous system, i.e. the driveline may be characterized as a CPS. The architecture is analyzed both from technical complexity and business strategy point of view.  The presented research indicates that a structured methodology which supports the development of the product architecture is needed at Scania, to enable control of the increasing technical complexity in the Cyber-Physical Systems. Finally, configuration rules are identified to be highly important in order to successfully realize a modular product architecture. A drawback with this approach is that the solution space becomes hard to identify, therefore a complete and flexible product description methodology is essential. The results from the case studies indicate that clustering of a Product Architecture DSM may result in a modular architecture with significantly reduced complexity, but with clusters that contain conflicting module drivers. It is also identified that the new modularization methodology is capable of identifying and proposing reasonable module candidates that address product complexity as well as company-specific strategies. Furthermore, several case studies show that the proposed method can be used for analyzing and finding the explicit and/or implicit, technical as well as strategic, reasons behind the architecture of an existing product.

abstract: Our daily life is becoming more and more reliant on services provided by the infrastructures power, gas , communication networks. Ensuring the security of these infrastructures is of utmost importance. This task becomes ever more challenging as the inter-dependence among these infrastructures grows and a security breach in one infrastructure can spill over to the others. The implication is that the security practices/ analysis recommended for these infrastructures should be done in coordination. This thesis, focusing on the power grid, explores strategies to secure the system that look into the coupling of the power grid to the cyber infrastructure, used to manage and control it, and to the gas grid, that supplies an increasing amount of reserves to overcome contingencies. The first part (Part I) of the thesis, including chapters 2 through 4, focuses on the coupling of the power and the cyber infrastructure that is used for its control and operations. The goal is to detect malicious attacks gaining information about the operation of the power grid to later attack the system. In chapter 2, we propose a hierarchical architecture that correlates the analysis of high resolution Micro-Phasor Measurement Unit (microPMU) data and traffic analysis on the Supervisory Control and Data Acquisition (SCADA) packets, to infer the security status of the grid and detect the presence of possible intruders. An essential part of this architecture is tied to the analysis on the microPMU data. In chapter 3 we establish a set of anomaly detection rules on microPMU data that flag "abnormal behavior". A placement strategy of microPMU sensors is also proposed to maximize the sensitivity in detecting anomalies. In chapter 4, we focus on developing rules that can localize the source of an events using microPMU to further check whether a cyber attack is causing the anomaly, by correlating SCADA traffic with the microPMU data analysis results. The thread that unies the data analysis in this chapter is the fact that decision are made without fully estimating the state of the system; on the contrary, decisions are made using a set of physical measurements that falls short by orders of magnitude to meet the needs for observability. More specifically, in the first part of this chapter (sections 4.1- 4.2), using microPMU data in the substation, methodologies for online identification of the source Thevenin parameters are presented. This methodology is used to identify reconnaissance activity on the normally-open switches in the substation, initiated by attackers to gauge its controllability over the cyber network. The applications of this methodology in monitoring the voltage stability of the grid is also discussed. In the second part of this chapter (sections 4.3-4.5), we investigate the localization of faults. Since the number of PMU sensors available to carry out the inference is insufficient to ensure observability, the problem can be viewed as that of under-sampling a "graph signal"; the analysis leads to a PMU placement strategy that can achieve the highest resolution in localizing the fault, for a given number of sensors. In both cases, the results of the analysis are leveraged in the detection of cyber-physical attacks, where microPMU data and relevant SCADA network traffic information are compared to determine if a network breach has affected the integrity of the system information and/or operations. In second part of this thesis (Part II), the security analysis considers the adequacy and reliability of schedules for the gas and power network. The motivation for scheduling jointly supply in gas and power networks is motivated by the increasing reliance of power grids on natural gas generators (and, indirectly, on gas pipelines) as providing critical reserves. Chapter 5 focuses on unveiling the challenges and providing solution to this problem. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2018

We describe the development of a cyber-physical system (Rehab Tracker) for improving patient compliance with at-home physical rehabilitation using neuromuscular electrical stimulation (NMES) therapy. Rehab Tracker consists of three components: 1) hardware modifications to sense and store use data from an FDA-approved NMES therapy device and provide Bluetooth communication capability, 2) an iOS-based smartphone/tablet application to receive and transmit NMES use data and serve as a conduit for patient-provider interactions and 3) a back-end server platform to receive device use data, display compliance data for provider review and provide automated positive and remedial push notifications to patients to improve compliance. This system allows for near real-time compliance monitoring via a secure web portal and offers a novel conduit for patient-provider communication during at-home rehabilitation to improve compliance. The system was tested in patients (n=5) who suffered anterior cruciate ligament rupture and surgical repair to provide proof-of-principal evidence for system functionality and an initial assessment of system usability. The system functioned as designed, recording 89% of rehabilitation sessions. Thus, Rehab Tracker is a functionally correct system with the potential to be used as a tool for studying NMES and mobile communication methodologies at scale and improving compliance with at-home rehabilitation programs.

abstract: The large distributed electric power system is a hierarchical network involving the transportation of power from the sources of power generation via an intermediate densely connected transmission network to a large distribution network of end-users at the lowest level of the hierarchy. At each level of the hierarchy (generation/ trans- mission/ distribution), the system is managed and monitored with a combination of (a) supervisory control and data acquisition (SCADA); and (b) energy management systems (EMSs) that process the collected data and make control and actuation de- cisions using the collected data. However, at all levels of the hierarchy, both SCADA and EMSs are vulnerable to cyber attacks. Furthermore, given the criticality of the electric power infrastructure, cyber attacks can have severe economic and social con- sequences. This thesis focuses on cyber attacks on SCADA and EMS at the transmission level of the electric power system. The goal is to study the consequences of three classes of cyber attacks that can change topology data. These classes include: (i) unobservable state-preserving cyber attacks that only change the topology data; (ii) unobservable state-and-topology cyber-physical attacks that change both states and topology data to enable a coordinated physical and cyber attack; and (iii) topology- targeted man-in-the-middle (MitM) communication attacks that alter topology data shared during inter-EMS communication. Specically, attack class (i) and (ii) focus on the unobservable attacks on single regional EMS while class (iii) focuses on the MitM attacks on communication links between regional EMSs. For each class of attacks, the theoretical attack model and the implementation of attacks are provided, and the worst-case attack and its consequences are exhaustively studied. In particularly, for class (ii), a two-stage optimization problem is introduced to study worst-case attacks that can cause a physical line over ow that is unobservable in the cyber layer. The long-term implication and the system anomalies are demonstrated via simulation. For attack classes (i) and (ii), both mathematical and experimental analyses sug- gest that these unobservable attacks can be limited or even detected with resiliency mechanisms including load monitoring, anomalous re-dispatches checking, and his- torical data comparison. For attack class (iii), countermeasures including anomalous tie-line interchange verication, anomalous re-dispatch alarms, and external contin- gency lists sharing are needed to thwart such attacks. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2015