A Resilient Control Approach to Secure Cyber Physical Systems (CPS) with an Application on Connected Vehicles

The objective of this dissertation is to develop a resilient control approach to secure Cyber Physical Systems (CPS) against cyber-attacks, network failures and potential physical faults. Despite being potentially beneficial in several aspects, the connectivity in CPSs poses a set of specific challenges from safety and reliability standpoint. The first challenge arises from unreliable communication network which affects the control/management of overall system. Second, faulty sensors and actuators can degrade the performance of CPS and send wrong information to the controller or other subsystems of the CPS. Finally, CPSs are vulnerable to cyber-attacks which can potentially lead to dangerous scenarios by affecting the information transmitted among various components of CPSs. Hence, a resilient control approach is proposed to address these challenges. The control approach consists of three main parts:(1) Physical fault diagnostics: This part makes sure the CPS works normally while there is no cyber-attacks/ network failure in the communication network; (2) Cyber-attack/failure resilient strategy: This part consists of a resilient strategy for specific cyber-attacks to compensate for their malicious effects ; (3) Decision making algorithm: The decision making block identifies the specific existing cyber-attacks/ network failure in the system and deploys corresponding control strategy to minimize the effect of abnormality in the system performance. In this dissertation, we consider a platoon of connected vehicle system under Co-operative Adaptive Cruise Control (CACC) strategy as a CPS and develop a resilient control approach to address the aforementioned challenges. The first part of this dissertation investigates fault diagnostics of connected vehicles assuming ideal communication network. Very few works address the real-time diagnostics problem in connected vehicles. This study models the effect of different faults in sensors and actuators, and also develops fault diagnosis scheme for detectable and identifiable faults. The proposed diagnostics scheme is based on sliding model observers to detect, isolate and estimate faults in the sensors and actuators. One of the main advantages of sliding model approach lies in applicability to nonlinear systems. Therefore, the proposed method can be extended for other nonlinear cyber physical systems as well. The second part of the proposed research deals with developing strategies to maintain performance of cyber-physical systems close to the normal, in the presence of common cyber-attacks and network failures. Specifically, the behavior of Dedicated Short-Range Communication (DSRC) network is analyzed under cyber-attacks and failures including packet dropping, Denial of Service (DOS) attack and false data injection attack. To start with, packet dropping in network communication is modeled by Bernoulli random variable. Then an observer based modifying algorithm is proposed to modify the existing CACC strategy against the effect of packet dropping phenomena. In contrast to the existing works on state estimation over imperfect communication network in CPS which mainly use either holding previous received data or Kalman filter with intermittent observation, a combination of these two approaches is used to construct the missing data over packet dropping phenomena. Furthermore, an observer based fault diagnostics based on sliding mode approach is proposed to detect, isolate and estimate sensor faults in connected vehicles platoon. Next, Denial of Service (DoS) attack is considered on the communication network. The effect of DoS attack is modeled as an unknown stochastic delay in data delivery in the communication network. Then an observer based approach is proposed to estimate the real data from the delayed measured data over the network. A novel approach based on LMI theory is presented to design observer and estimate the states of the system via delayed measurements. Next, we explore and alternative approach by modeling DoS with unknown constant time delay and propose an adaptive observer to estimate the delay. Furthermore, we study the effects of system uncertainties on the DoS algorithm. In the third algorithm, we considered a general CPS with a saturated DoS attack modeled with constant unknown delay. In this part, we modeled the DoS via a PDE and developed a PDE based observer to estimate the delay as well as states of the system while the only available measurements are delayed. Furthermore, as the last cyber-attack of the second part of the dissertation, we consider false data injection attack as the fake vehicle identity in the platoon of vehicles. In this part, we develop a novel PDE-based modeling strategy for the platoon of vehicles equipped with CACC. Moreover, we propose a PDE based observer to detect and isolate the location of the false data injection attack injected into the platoon as fake identity. Finally, the third part of the dissertation deals with the ongoing works on an optimum decision making strategy formulated via Model Predictive Control (MPC). The decision making block is developed to choose the optimum strategy among available strategies designed in the second part of the dissertation

From Digital Twins to Digital Selves and Beyond

This open access book aims at deepening the understanding of the relation between cyber-physical systems (CPSs) as socio-technical systems and their digital representations with intertwined artificial intelligence (AI). The authors describe why it is crucial for digital selves to be able to develop emotional behavior and why a humanity-inspired AI is necessary so that humans and humanoids can coexist. The introductory chapter describes major milestones in computer science which form the basis for the implementation of digital twins and digital selves. The subsequent Part I then lays the foundation to develop a socio-technical understanding of the nature of digital twins as representations and trans-human development objects. Following the conceptual understanding of digital twins and how they could be engineered according to cognitive and organizational structures, Part II forms the groundwork for understanding social behavior and its modeling. It discusses various perception-based socio-emotional approaches before sketching behavior-relevant models and their simulation capabilities. In particular, it is shown how emotions can substantially influence the collective behavior of artificial actors. Part III eventually presents a symbiosis showing under which preconditions digital selves might construct and produce digital twins as integrated design elements in trans-human ecosystems. The chapters in this part are dedicated to opportunities and modes of co-creating reflective socio-trans-human systems based on digital twin models, exploring mutual control and continuous development. The final epilog is congenitally speculative in its nature by presenting thoughts on future developments of artificial life in computational substrates. The book is written for researchers and professionals in areas like cyber-physical systems, robotics, social simulation or systems engineering, interested to take a speculative look into the future of digital twins and autonomous agents. It also touches upon philosophical aspects of digital twins, digital selves and humanoids

From Digital Twins to Digital Selves and Beyond

This open access book aims at deepening the understanding of the relation between cyber-physical systems (CPSs) as socio-technical systems and their digital representations with intertwined artificial intelligence (AI). The authors describe why it is crucial for digital selves to be able to develop emotional behavior and why a humanity-inspired AI is necessary so that humans and humanoids can coexist. The introductory chapter describes major milestones in computer science which form the basis for the implementation of digital twins and digital selves. The subsequent Part I then lays the foundation to develop a socio-technical understanding of the nature of digital twins as representations and trans-human development objects. Following the conceptual understanding of digital twins and how they could be engineered according to cognitive and organizational structures, Part II forms the groundwork for understanding social behavior and its modeling. It discusses various perception-based socio-emotional approaches before sketching behavior-relevant models and their simulation capabilities. In particular, it is shown how emotions can substantially influence the collective behavior of artificial actors. Part III eventually presents a symbiosis showing under which preconditions digital selves might construct and produce digital twins as integrated design elements in trans-human ecosystems. The chapters in this part are dedicated to opportunities and modes of co-creating reflective socio-trans-human systems based on digital twin models, exploring mutual control and continuous development. The final epilog is congenitally speculative in its nature by presenting thoughts on future developments of artificial life in computational substrates. The book is written for researchers and professionals in areas like cyber-physical systems, robotics, social simulation or systems engineering, interested to take a speculative look into the future of digital twins and autonomous agents. It also touches upon philosophical aspects of digital twins, digital selves and humanoids

Bibliographical review on cyber attacks from a control oriented perspective

This paper presents a bibliographical review of definitions, classifications and applications concerning cyber attacks in networked control systems (NCSs) and cyber-physical systems (CPSs). This review tackles the topic from a control-oriented perspective, which is complementary to information or communication ones. After motivating the importance of developing new methods for attack detection and secure control, this review presents security objectives, attack modeling, and a characterization of considered attacks and threats presenting the detection mechanisms and remedial actions. In order to show the properties of each attack, as well as to provide some deeper insight into possible defense mechanisms, examples available in the literature are discussed. Finally, open research issues and paths are presented.Peer ReviewedPostprint (author's final draft

Information fusion architectures for security and resource management in cyber physical systems

Data acquisition through sensors is very crucial in determining the operability of the observed physical entity. Cyber Physical Systems (CPSs) are an example of distributed systems where sensors embedded into the physical system are used in sensing and data acquisition. CPSs are a collaboration between the physical and the computational cyber components. The control decisions sent back to the actuators on the physical components from the computational cyber components closes the feedback loop of the CPS. Since, this feedback is solely based on the data collected through the embedded sensors, information acquisition from the data plays an extremely vital role in determining the operational stability of the CPS. Data collection process may be hindered by disturbances such as system faults, noise and security attacks. Hence, simple data acquisition techniques will not suffice as accurate system representation cannot be obtained. Therefore, more powerful methods of inferring information from collected data such as Information Fusion have to be used. Information fusion is analogous to the cognitive process used by humans to integrate data continuously from their senses to make inferences about their environment. Data from the sensors is combined using techniques drawn from several disciplines such as Adaptive Filtering, Machine Learning and Pattern Recognition. Decisions made from such combination of data form the crux of information fusion and differentiates it from a flat structured data aggregation. In this dissertation, multi-layered information fusion models are used to develop automated decision making architectures to service security and resource management requirements in Cyber Physical Systems --Abstract, page iv

Survivability modeling for cyber-physical systems subject to data corruption

Cyber-physical critical infrastructures are created when traditional physical infrastructure is supplemented with advanced monitoring, control, computing, and communication capability. More intelligent decision support and improved efficacy, dependability, and security are expected. Quantitative models and evaluation methods are required for determining the extent to which a cyber-physical infrastructure improves on its physical predecessors. It is essential that these models reflect both cyber and physical aspects of operation and failure. In this dissertation, we propose quantitative models for dependability attributes, in particular, survivability, of cyber-physical systems. Any malfunction or security breach, whether cyber or physical, that causes the system operation to depart from specifications will affect these dependability attributes. Our focus is on data corruption, which compromises decision support -- the fundamental role played by cyber infrastructure. The first research contribution of this work is a Petri net model for information exchange in cyber-physical systems, which facilitates i) evaluation of the extent of data corruption at a given time, and ii) illuminates the service degradation caused by propagation of corrupt data through the cyber infrastructure. In the second research contribution, we propose metrics and an evaluation method for survivability, which captures the extent of functionality retained by a system after a disruptive event. We illustrate the application of our methods through case studies on smart grids, intelligent water distribution networks, and intelligent transportation systems. Data, cyber infrastructure, and intelligent control are part and parcel of nearly every critical infrastructure that underpins daily life in developed countries. Our work provides means for quantifying and predicting the service degradation caused when cyber infrastructure fails to serve its intended purpose. It can also serve as the foundation for efforts to fortify critical systems and mitigate inevitable failures --Abstract, page iii

Learning-based Predictive Control Approach for Real-time Management of Cyber-physical Systems

Cyber-physical systems (CPSs) are composed of heterogeneous, and networked hardware and software components tightly integrated with physical elements [72]. Large-scale CPSs are composed of complex components, subject to uncertainties [89], as though their design and development is a challenging task. Achieving reliability and real-time adaptation to changing environments are some of the challenges involved in large-scale CPSs development [51]. Addressing these challenges requires deep insights into control theory and machine learning. This research presents a learning-based control approach for CPSs management, considering their requirements, specifications, and constraints. Model-based control approaches, such as model predictive control (MPC), are proven to be efficient in the management of CPSs [26]. MPC is a control technique that uses a prediction model to estimate future dynamics of the system and generate an optimal control sequence over a prediction horizon. The main benefit of MPC in CPSs management comes from its ability to take the predictions of system’s environmental conditions and disturbances into account [26]. In this dissertation, centralized and distributed MPC strategies are designed for the management of CPSs. They are implemented for the thermal management of a CPS case study, smart building. The control goals are optimizing system efficiency (lower thermal power consumption in the building), and improving users’ convenience (maintaining desired indoor thermal conditions in the building). Model-based control strategies are advantageous in the management of CPSs due to their ability to provide system robustness and stability. The performance of a model-based controller strongly depends on the accuracy of the model as a representation of the system dynamics [26]. Accurate modeling of large-scale CPSs is difficult (due to the existence of unmodeled dynamics and uncertainties in the modeling process); therefore, modelbased control approach is not practical for these systems [6]. By incorporating machine learning with model-based control strategies, we can address CPS modeling challenges while preserving the advantages of model-based control methods. In this dissertation, a learning-based modeling strategy incorporated with a model-based control approach is proposed to manage energy usage and maintain thermal, visual, and olfactory performance in buildings. Neural networks (NNs) are used to learn the building’s performance criteria, occupant-related parameters, environmental conditions, and operation costs. Control inputs are generated through the model-based predictive controller and based on the learned parameters, to achieve the desired performance. In contrast to the existing building control systems presented in the literature, the proposed management system integrates current and future information of occupants (convenience, comfort, activities), building energy trends, and environment conditions (environmental temperature, humidity, and light) into the control design. This data is synthesized and evaluated in each instance of decision-making process for managing building subsystems. Thus, the controller can learn complex dynamics and adapt to the changing environment, to achieve optimal performance while satisfying problem constraints. Furthermore, while many prior studies in the filed are focused on optimizing a single aspect of buildings (such as thermal management), and little attention is given to the simultaneous management of all building objectives, our proposed management system is developed considering all buildings’ physical models, environmental conditions, comfort specifications, and occupants’ preferences, and can be applied to various building management applications. The proposed control strategy is implemented to manage indoor conditions and energy consumption in a building, simulated in EnergyPlus software. In addition, for comparison purposes, we designed and simulated a baseline controller for the building under the same conditions