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<p><em>Imbulgoda Liyangahawatte, Gihan Janith Mendis Ph.D., Purdue University, May</em></p> <p><em>2023. Deep learning for securing critical infrastructure with the emphasis on power</em></p> <p><em>systems and wireless communication. Major Professor: Dr. Jin Kocsis.</em></p> <p><br></p> <p><em>Critical infrastructures, such as power systems and communication</em></p> <p><em>infrastructures, are of paramount importance to the welfare and prosperity of</em></p> <p><em>modern societies. Therefore, critical infrastructures have a high vulnerability to</em></p> <p><em>attacks from adverse parties. Subsequent to the advancement of cyber technologies,</em></p> <p><em>such as information technology, embedded systems, high-speed connectivity, and</em></p> <p><em>real-time data processing, the physical processes of critical infrastructures are often</em></p> <p><em>monitored and controlled through cyber systems. Therefore, modern critical</em></p> <p><em>infrastructures are often viewed as cyber-physical systems (CPSs). Incorporating</em></p> <p><em>cyber elements into physical processes increases efficiency and control. However, it</em></p> <p><em>also increases the vulnerability of the systems to potential cybersecurity threats. In</em></p> <p><em>addition to cyber-level attacks, attacks on the cyber-physical interface, such as the</em></p> <p><em>corruption of sensing data to manipulate physical operations, can exploit</em></p> <p><em>vulnerabilities in CPSs. Research on data-driven security methods for such attacks,</em></p> <p><em>focusing on applications related to electrical power and wireless communication</em></p> <p><em>critical infrastructure CPSs, are presented in this dissertation. As security methods</em></p> <p><em>for electrical power systems, deep learning approaches were proposed to detect</em></p> <p><em>adversarial sensor signals targeting smart grids and more electric aircraft.</em></p> <p><em>Considering the security of wireless communication systems, deep learning solutions</em></p> <p><em>were proposed as an intelligent spectrum sensing approach and as a primary user</em></p> <p><em>emulation (PUE) attacks detection method on the wideband spectrum. The recent</em></p> <p><em>abundance of micro-UASs can enable the use of weaponized micro-UASs to conduct</em></p> <p><em>physical attacks on critical infrastructures. As a solution for this, the radio</em></p> <p><em>frequency (RF) signal-analyzing deep learning method developed for spectrum</em></p> <p><em>sensing was adopted to realize an intelligent radar system for micro-UAS detection.</em></p> <p><em>This intelligent radar can be used to provide protection against micro-UAS-based</em></p> <p><em>physical attacks on critical infrastructures.</em></p>

This thesis aims to develop and evaluate a dashboard design that visualizes a stream of data from the different entities involved in autonomous warehouses, a subset of cyber-physical systems. I created this dashboard through User-Centered Design (UCD) methodologies based on two feedback iterations with the stakeholders employing semi-structured expert opinion interviews. This thesis also discusses the different stages involved in building this dashboard design, the design decisions, the technical aspects of the libraries used, and the feedback session towards the end of the project. It also presents the implemented dashboard as a proof of development efforts and explains its different functionalities. The project concludes with evaluating the dashboard through a semi-structured interview with the respective stakeholders and suggests features for further development. / Denna studie ämnar att utveckla och utvärdera en design för ett dashboard som visualiserar dataströmmar från olika enheter som kan hittas i autonoma lager. Detta dashboard har utvecklats genom att använda metoder inom användarcentrerad design, som baserades på två iterationer med intressenter som är experter inom området, där semistrukturerade intervjuer gjordes. Denna studie diskuterar också de olika steg som är involverade i att bygga designen av detta dashboard, de olika beslut som togs i designprocessen, de tekniska aspekterna av de bibliotek som används och resultatet från de sessioner som hölls för att få feedback i slutet av projektet. Studien presenterar också det dashboard som utvecklades samt förklarar dess funktionalitet. Slutsatser dras från de semistrukturerade intervjuerna med respektive intressent och föreslår framtida funktioner som skulle vara möjliga att implementera.

Autonomous Driving Systems (ADS), a subcategory of Cyber-Physical Systems (CPS) are becoming increasingly popular with ubiquitous deployment. They provide advanced operational functions for perception and control, but this also raises the question of their safety capability. Such questions include if the vehicle can stay within its lane, keep a safe distance from the leading vehicle, or avoid obstacles, especially under the presence of uncertainties. In this master thesis, the operational safety of ADS will be addressed, more specifically on the Adaptive Cruise Control (ACC) system by modeling an optimal control problem based on Control Barrier Function (CBF) unified with Model Predictive Control (MPC). The corresponding optimal control problem is robust against measurement uncertainties for an Autonomous Vehicle (AV) driving on a highway, where the measurement uncertainties will represent the common faults in the perception system of the AV. A Kalman Filter (KF) is also added to the system to investigate the performance difference. The resulting framework is implemented and evaluated on a simulation scenario created in the open-source autonomous driving simulator CARLA. Simulations show that MPC-CBF is indeed robust against measurement uncertainties for well-selected horizon and slack variable values. The simulations also show that adding a KF improves the overall performance. The higher the horizon, the more confident the system becomes as the distance to the leading vehicle decreases. However, this may cause infeasibility where there are no solutions to the optimal control problem during sudden braking as the AV cannot brake fast enough before it crashes. Meanwhile, the smaller the slack variable, the more restrictive becomes CBF where it impacts more on the control input than desired which could also cause infeasibility. The results of this thesis will help to facilitate safety-critical CPS development to be deployed in real-world applications. / Autonoma körsystem (ADS), som är en del av cyberfysiska system (CPS), har blivit alltmer populär med allestädes närvarande användning. Det bidra med avancerade operativa funktioner för perception och styrning, men samtidig väcker detta också frågan om dess säkerhetsförmåga. Sådana frågor inkluderar om fordonet kan hålla sig inom sitt körfält, om det kan hålla ett säkert avstånd till det ledande fordonet eller om det kan undvika hinder, speciellt under osäkerheter hos systemet. I detta examensarbete kommer driftsäkerheten hos ADS att behandlas, mer specifik på adaptiv farthållare (ACC) genom att modellera ett optimalt kontrollproblem baserat på kontrollbarriärfunktion (CBF) förenat med modellförutsägande styrning (MPC). Motsvarande optimalt kontrollproblem är robust mot mätosäkerheter för ett autonomt fordon som kör på en motorväg, där mätosäkerheterna representerar vanliga fel i AV:s perceptionssystem. Ett Kalmanfilter (KF) läggs också till i systemet för att undersöka skillnaden i prestanda. Det resulterande ramverket implementeras och utvärderas på ett simuleringsscenario som skapats i den öppna källkodssimulatorn för autonom körning CARLA. Simulationer visar att MPC-CBF är robust mot mätosäkerheter för väl valda värden för horisont och slackvariabler. Det visar också att systemets prestanda förbättrats ännu mer om ett KF läggs till. Ju större horisont, desto mer självsäkert blir systemet när avståndet till det ledande fordonet minskar. Detta kan dock leda till att det inte finns några lösningar på det optimala kontrollproblemet vid plötslig inbromsning, eftersom fordonet inte hinner bromsa tillräckligt snabbt innan det kraschar. Ju mindre slackvariabeln är, desto mer restriktiv blir CBF som påverkar styrningen mer än vad som är önskvärt vilket också kan leda till olösbart optimalt kontrollproblem. Resultatet från detta examensarbete bär syftet att gynna utvecklingen av säkerhetkritisk CPS som ska användas i praktiska tillämpningar.

Stop-and-Go Adaptive Cruise Control (ACC+) is an extension of Adaptive Cruise Control (ACC) that works at low speed as well as normal highway speeds to regulate the speed of the vehicle relative to the vehicle it is following. In this thesis, we design an ACC+ controller for a scale model electric vehicle that ensures the robust performance of the system under various models of uncertainty. We capture the operation of the hybrid system via a state-chart model that performs mode switching between different digital controllers with additional decision logic to guarantee the collision freedom of the system under normal operation. We apply different controller design methods such as Linear Quadratic Regulator (LQR) and H-infinity and perform multiple simulation runs in MATLAB/Simulink to validate the performance of the proposed designs. We compare the practicality of our design with existing formally verified ACC designs from the literature. The comparisons show that the other formally verified designs exhibit unacceptable behaviour in the form of mode thrashing that produces excessive acceleration and deceleration of the vehicle. While simulations provide some assurance of safe operation of the system design, they do not guarantee system safety under all possible cases. To increase confidence in the system, we use Differential Dynamic Logic (dL) to formally state environmental assumptions and prove safety goals, including collision freedom. The verification is done in two stages. First, we identify the invariant required to ensure the safe operation of the system and we formally verify that the invariant preserves the safety property of any system with similar dynamics. This procedure provides a high level abstraction of a class of safe solutions for ACC+ system designs. Second, we show that our ACC+ system design is a refinement of the abstract model. The safety of the closed loop ACC+ system is proven by verifying bounds on the system variables using the KeYmaera verification tool for hybrid systems. The thesis demonstrates how practical ACC+ controller designs optimized for fuel economy, passenger comfort, etc., can be verified by showing that they are a refinement of the abstract high level design. / Thesis / Master of Applied Science (MASc)

Low-power wireless technology is a part and parcel of our daily life, shaping the way in which we behave, interact, and more generally live. The ubiquity of cheap, tiny, battery-powered devices augmented with sensing, actuation, and wireless communication capabilities has given rise to a ``smart" society, where people, machines, and objects are seamlessly interconnected, among themselves and with the environment. Behind the scenes, low-power wireless protocols are what enables and rules all interactions, organising these embedded devices into wireless networks, and orchestrating their communications. The recent years have witnessed a persistent increase in the pervasiveness and impact of low-power wireless. After having spawned a wide spectrum of powerful applications in the consumer domain, low-power wireless solutions are extending their influence over the industrial context, where their adoption as part of feedback control loops is envisioned to revolutionise the production process, paving the way for the Fourth Industrial Revolution. However, as the scale and relevance of low-power wireless systems continue to grow, so do the challenges posed to the communication substrates, required to satisfy ever more strict requirements in terms of reliability, responsiveness, and energy consumption. Harmonising these conflicting demands is far beyond what is enabled by current network stacks and control architectures; the need to timely bridge this gap has spurred a new wave of interest in low-power wireless networking, and directly motivated our work. In this thesis, we take on this challenge with a main conceptual and technical tool: concurrent transmissions (CTX), a technique that, by enforcing nodes to transmit concurrently, has been shown to unlock unprecedented fast, reliable, and energy efficient multi-hop communications in low-power wireless networks, opening new opportunities for protocol design. We first direct our research endeavour towards industrial applications, focusing on the popular IEEE 802.15.4 narrowband PHY layer, and advance the state of the art along two different directions: interference resilience and aperiodic wireless control. We tackle radio-frequency noise by extensively analysing, for the first time, the dependability of CTX under different types, intensities, and distributions of reproducible interference patterns, and by devising techniques to push it further. Specifically, we concentrate on CRYSTAL, a recently proposed communication protocol that relies on CTX to rapidly and dependably collect aperiodic traffic. By integrating channel hopping and noise detection in the protocol operation, we provide a novel communication stack capable of supporting aperiodic transmissions with near-perfect reliability and a per-mille radio duty cycle despite harsh external interference. These results lay the ground towards the exploitation of CTX for aperiodic wireless control; we explore this research direction by co-designing the Wireless Control Bus (WCB), our second contribution. WCB is a clean-slate CTX-based communication stack tailored to event-triggered control (ETC), an aperiodic control strategy holding the capability to significantly improve the efficiency of wireless control systems, but whose real-world impact has been hampered by the lack of appropriate networking support. Operating in conjunction with ETC, WCB timely and dynamically adapts the network operation to the control demands, unlocking an order-of-magnitude reduction in energy costs w.r.t. traditional periodic approaches while retaining the same control performance, therefore unleashing and concretely demonstrating the true ETC potential for the first time. Nevertheless, low-power wireless communications are rapidly evolving, and new radios striking novel trade-offs are emerging. Among these, in the second part of the thesis we focus on ultra-wideband (UWB). By providing hitherto missing networking primitives for multi-hop dissemination and collection over UWB, we shed light on the communication potentialities opened up by the high data throughput, clock precision, and noise resilience offered by this technology. Specifically, as a third contribution, we demonstrate that CTX not only can be successfully exploited for multi-hop UWB communications but, once embodied in a full-fledged system, provide reliability and energy performance akin to narrowband. Furthermore, the higher data rate and clock resolution of UWB chips unlock up to 80% latency reduction w.r.t. narrowband CTX, along with orders-of-magnitude improvements in network-wide time synchronization. These results showcase how UWB CTX could significantly benefit a multitude of applications, notably including low-power wireless control. With WEAVER, our last contribution, we make an additional step towards this direction, by supporting the key functionality of data collection with an ultra-fast convergecast stack for UWB. Challenging the internal mechanics of CTX, WEAVER interleaves data and acknowledgements flows in a single, self-terminating network-wide flood, enabling the concurrent collection of different packets from multiple senders with unprecedented latency, reliability, and energy efficiency. Overall, this thesis pushes forward the applicability and performance of low-power wireless, by contributing techniques and protocols to enhance the dependability, timeliness, energy efficiency, and interference resilience of this technology. Our research is characterized by a strong experimental slant, where the design of the systems we propose meets the reality of testbed experiments and evaluation. Via our open-source implementations, researchers and practitioners can directly use, extend, and build upon our contributions, fostering future work and research on the topic.

<p>Real-time hybrid simulation (RTHS) has demonstrated certain advantages over conventional large-scale testing. In an RTHS, the system that is under study is partitioned into a numerical and a physical substructure, where the numerical part is comprised of those elements that are easier to model mathematically, while the physical part consists of those that present a complex behavior difficult to capture in a numerical model. The most complex part of this study is the isolation system, a technology used to protect structures against earthquakes by modifying how they respond to ground motions. Unbonded Fiber Reinforced Elastomeric Isolators (UFREIs) are devices that can accomplish this task and have gained attention in recent years because of their modest but valuable features that make them suitable for implementation in low-rise buildings and in developing countries because of their low cost. Our end goal for this work is to enable the testing of scaled versions of these elastomeric isolators to understand their behavior under shear tests and realistic loading. </p> <p>A testing instrument was designed and constructed to apply a uniaxial compressive force up to 22kN and a shear force of 8kN simultaneously to the specimens. A testing program was conducted where four primary sources of signal distortion were identified as caused by the servo-hydraulic system. From these results, a mechanics-based model was developed to understand better the dynamics that the sliding table can introduce to the measured signals accounting for inertial and dissipative forces. Two Bouc-Wen models were implemented to simulate the behavior of the UFREIs. The first only accounts for the hysteretic behavior of the isolator, and the second accounts for the additional nonlinearities found in the isolator’s behavior. These models were assembled in a virtual RTHS which is available to users interested in learning the applications of RTHS of a base-isolated structure with a nonlinear component.</p> <p>An RTHS experiment was conducted in the IISL where the control system comprised a delay compensator and a proportional-integral controller, which exhibited a good tracking performance with minimal delay and low RMSE. However, it can increase the distortion of the oil-column resonance in the measured signals. The simulation captures the behavior of the isolated structure for small displacements. However, it underestimates the displacement of the full-scale specimen for large displacements. The RTHS showed a better approximation of the displacement of the full-scale structure than the theoretical behavior approximated by the Bouc-Wen models.</p>

The manufacturing industry is competitive and is constantly striving to improve OEE. In the transition to smart production, digital technologies such as IIoT are highlighted as important. IIoT platforms enable real-time monitoring. In this sense, digital technologies such as IIoT are expected to improve OEE by enabling the analysis of real-time data and production availability.  A qualitative study with an abductive approach has been conducted. The empirical material has been collected through a case study of a heavy-duty vehicle industry and the theoretical framework is based on a literature study. Lastly, a thematic analysis has been used for the derivation of appropriate themes for analysis. The study concluded that challenges and enablers related to the implementation of IIoT to improve OEE can be divided into technical and cultural factors. Technical challenges and enablers mainly consider the achievement of interoperability, compatibility, and cyber security, while cultural factors revolve around digital acceptance, competence, encouragement of digital curiosity, and creating knowledge and understanding towards OEE. Lastly, conclusions can be drawn that implementation of IIoT has a positive effect on OEE since it ensures consistent and accurate data, which lies a solid foundation for production decisions. Also, digitalization of production enhances lean practices which are considered a key element for improving OEE.

More dynamic markets, shorter product life cycles and comprehensive variant management are challenges that dominate today's market. These maxims apply to the automotive sector, which is currently highly exposed to trade wars, changing mobility patterns and the emergence of new technologies and competitors. To meet these challenges, this thesis presents the creation of a digital twin of an existing production line of electric motors using discrete event simulation. Based on a detailed literature research, a step-by-step establishment of the simulation model of the production line using the software Plant Simulation is presented and argued. Finally, different experiments are carried out with the created model to show how a production line can be examined and optimized by means ofsimulation using different parameters. Within the scope of the different experiments regarding the number of workpiece carriers, number of operators as well as buffer sizes, the line was examined concerning the increase of the output. Furthermore, the simulation model was used to make decisions for future investments in additional XXX machines. Four different scenarios were examined and optimized. By examining the different parameters, optimization potentials of XXX% in the first scenario and up to XXX% in the fourth scenario were achieved. Finally, it was proven that the developed simulation model can be used as a tool for optimizing an existing production line and can generate useful investment information. Beyond that, the development of the simulation model can be employed to investigate further business questions at hand for the specific production line in question. / Mer dynamiska marknader, kortare produktlivscykler och omfattande varianthantering är utmaningar som dominerar dagens marknad. Dessa maximer gäller bilindustrin, som för närvarande är mycket utsatt för handelskrig, förändrade rörlighetsmönster och framväxten av ny teknik och nya konkurrenter. För att möta dessa utmaningar innebär denna avhandling skapandet av en digital tvilling av en befintlig produktionslinje av elmotorer med diskret händelsesimulering. Baserat på en detaljerad litteraturforskning presenteras och argumenteras en steg-för-steg-etablering av simuleringsmodellen för produktionslinjen med hjälp av programvaran Plant Simulation. Slutligen utförs olika experiment med den skapade modellen för att visa hur en produktionslinje kan undersökas och optimeras med hjälp av simulering med hjälp av olika parametrar. Inom ramen för de olika experimenten när det gäller antalet arbetsstyckesbärare, antalet operatörer samt buffertstorlekar undersöktes linjen om ökningen av produktionen. Dessutom användes simuleringsmodellen för att fatta beslut för framtida investeringar i ytterligare hårnålsmaskiner. Fyra olika scenarier undersöktes och optimerades. Genom att undersöka de olika parametrarna uppnåddes optimeringspotentialer på XXX % i det första scenariot och upp till XXX % i det fjärde scenariot. Slutligen bevisades det att den utvecklade simuleringsmodellen kan användas som ett verktyg för att optimera en befintlig produktionslinje och kan generera användbar investeringsinformation. Utöver detta kan utvecklingen av simuleringsmodellen användas för att undersöka ytterligare affärsfrågor till hands för den specifika produktionslinjen i fråga.

<p dir="ltr">In recent years, a surge in data-driven computation has significantly impacted security analysis in cyber-physical systems (CPSs), especially in decentralized environments. This transformation can be attributed to the remarkable computational power offered by high-performance computers (HPCs), coupled with advancements in distributed computing techniques and sophisticated learning algorithms like deep learning and reinforcement learning. Within this context, wireless communication systems and decentralized computing systems emerge as highly suitable environments for leveraging data-driven computation in security analysis. Our research endeavors have focused on exploring the vast potential of various deep learning algorithms within the CPS domains. We have not only delved into the intricacies of existing algorithms but also designed novel approaches tailored to the specific requirements of CPSs. A pivotal aspect of our work was the development of a comprehensive decentralized computing platform prototype, which served as the foundation for simulating complex networking scenarios typical of CPS environments. Within this framework, we harnessed deep learning techniques such as restricted Boltzmann machine (RBM) and deep convolutional neural network (DCNN) to address critical security concerns such as the detection of Quality of Service (QoS) degradation and Denial of Service (DoS) attacks in smart grids. Our experimental results showcased the superior performance of deep learning-based approaches compared to traditional pattern-based methods. Additionally, we devised a decentralized computing system that encompassed a novel decentralized learning algorithm, blockchain-based learning automation, distributed storage for data and models, and cryptography mechanisms to bolster the security and privacy of both data and models. Notably, our prototype demonstrated excellent efficacy, achieving a fine balance between model inference performance and confidentiality. Furthermore, we delved into the integration of domain knowledge from CPSs into our deep learning models. This integration shed light on the vulnerability of these models to dedicated adversarial attacks. Through these multifaceted endeavors, we aim to fortify the security posture of CPSs while unlocking the full potential of data-driven computation in safeguarding critical infrastructures.</p>