<p>Can a critical industrial system, such as a nuclear reactor, be made self-aware and cognizant of its operational history? Can it alert authorities covertly to malicious intrusion without exposing its  defense  mechanisms?  What  if  the  intruders  are  highly  knowledgeable  adversaries,  or  even  insiders that may have designed the system? This thesis addresses these research questions through a novel physical process defense called Covert Cognizance (C2). </p> <p>C2  serves  as  a  last  line  of  defense  to  industrial  systems  when  existing  information  and  operational technology defenses have been breached by advanced persistent threat (APT) actors or insiders. It is an active form of defense that may be embedded in an existing system to induce intelligence,  i.e.,  self-awareness,  and  make  various subsystems  aware  of  each  other.  It  interacts with the system at the process level and provides an additional layer of security to the process data therein without the need of a human in the loop. </p> <p>The C2 paradigm is  founded on two core requirements – zero-impact and zero-observability. Departing from contemporary active defenses, zero-impact requires a successful implementationto leave no footprint on the system ensuring identical operation while zero-observability requires that the embedding is immune to pattern-discovery algorithms.  In other words, a third-party such as  a  malicious  intruder  must  be  unable  to  detect  the  presence  of  the  C2  defense  based  on  observation of the process data, even when augmented by machine learning tools that are adept at pattern discovery. </p> <p>In the present work, nuclear reactor simulations are embedded with the C2 defense to induce awareness across subsystems and defend them against highly knowledgeable adversaries that have bypassed existing safeguards such as model-based defenses.  Specifically, the subsystems are made aware  of  each  other  by  embedding  critical information from  the  process  variables  of  one sub-module  along  the  noise of  the  process  variables  of  another,  thus  rendering  the  implementation  covert and  immune  to  pattern  discovery.   The  implementation  is  validated  using  generative adversarial  nets,  representing  a  state-of-the-art  machine  learning  tool,  and  statistical  analysis  of  the  reactor  states,  control  inputs,  outputs  etc. The  work  is  also  extended  to  data  masking  applications  via  the  deceptive  infusion  of  data  (DIOD)  paradigm.  Future  work  focuses  on  the  development of automated C2 modules for “plug ‘n’ play” deployment onto critical infrastructure and/or their digital twins.</p>

<p dir="ltr">The onset of smart manufacturing signifies a crucial shift in the industrial landscape, underscoring the pressing need for systems capable of adapting to and managing the complex dynamics of modern production environments. In this context, the importance of smart monitoring becomes increasingly apparent, serving as a vital tool for ensuring operational efficiency and reliability. Inspired by the critical role of auditory perception in human decision-making, this study investigated the application of machine sound recognition for practical use in manufacturing environments. Addressing the challenge of utilizing machine sounds in the loud noises of factories, the study employed an Internal Sound Sensor (ISS).</p><p dir="ltr">The study examined how sound propagates through structures and further explored acoustic characteristics of the ISS, aiming to apply these findings in machine monitoring. To leverage the ISS effectively and achieve a higher level of monitoring, a smart sound monitoring framework was proposed to integrate sound monitoring with machine data and human-machine interface. Designed for applicability and cost effectiveness, this system employs real-time edge computing, making it adaptable for use in various industrial settings.</p><p dir="ltr">The proposed framework and ISS deployed across a diverse range of production environments, showcasing a leap forward in the integration of smart technologies in manufacturing. Their application extends beyond continuous manufacturing to include discrete manufacturing systems, demonstrating adaptability. By analyzing sound signals from various production equipment, this study delves into developing machine sound recognition models that predict operational states and productivity, aiming to enhance manufacturing efficiency and oversight on real factory floors. This comprehensive and practical approach underlines the framework's potential to revolutionize operational management and manufacturing productivity. The study progressed to integrating manufacturing context with sound data, advancing towards high-level monitoring for diagnostic predictions and digital twin. This approach confirmed sound recognition's role in manufacturing diagnostics, laying a foundation for future smart monitoring improvements.</p>

Les systèmes navals représentent une infrastructure stratégique pour le commerce international et les activités militaires. Ces systèmes sont de plus en plus informatisés afin de réaliser une navigation optimale et sécurisée. Pour atteindre cet objectif, une grande variété de systèmes embarqués génèrent différentes informations sur la navigation et l'état des composants, ce qui permet le contrôle et le monitoring à distance. Du fait de leur importance et de leur informatisation, les systèmes navals sont devenus une cible privilégiée des pirates informatiques. Par ailleurs, la mer est un environnement rude et incertain qui peut produire des dysfonctionnements. En conséquence, la prise de décisions basée sur des fausses informations à cause des anomalies, peut être à l'origine de répercussions potentiellement catastrophiques.Du fait des caractéristiques particulières de ces systèmes, les méthodologies classiques de détection d'anomalies ne peuvent pas être appliquées tel que conçues originalement. Dans cette thèse nous proposons les mesures de qualité comme une potentielle alternative. Une méthodologie adaptée aux systèmes cyber-physiques a été définie pour évaluer la qualité des flux de données générés par les composants de ces systèmes. À partir de ces mesures, une nouvelle approche pour l'analyse de scénarios fonctionnels a été développée. Des niveaux d'acceptation bornent les états de normalité et détectent des mesures aberrantes. Les anomalies examinées par composant permettent de catégoriser les détections et de les associer aux catégories définies par le modèle proposé. L'application des travaux à 13 scénarios créés pour une plate-forme composée par deux cuves et à 11 scénarios pour deux drones aériens a servi à démontrer la pertinence et l'intérêt de ces travaux. / Naval systems represent a strategic infrastructure for international commerce and military activity. Their protection is thus an issue of major importance. Naval systems are increasingly computerized in order to perform an optimal and secure navigation. To attain this objective, on board vessel sensor systems provide navigation information to be monitored and controlled from distant computers. Because of their importance and computerization, naval systems have become a target for hackers. Maritime vessels also work in a harsh and uncertain operational environments that produce failures. Navigation decision-making based on wrongly understood anomalies can be potentially catastrophic.Due to the particular characteristics of naval systems, the existing detection methodologies can't be applied. We propose quality evaluation and analysis as an alternative. The novelty of quality applications on cyber-physical systems shows the need for a general methodology, which is conceived and examined in this dissertation, to evaluate the quality of generated data streams. Identified quality elements allow introducing an original approach to detect malicious acts and failures. It consists of two processing stages: first an evaluation of quality; followed by the determination of agreement limits, compliant with normal states to identify and categorize anomalies. The study cases of 13 scenarios for a simulator training platform of fuel tanks and 11 scenarios for two aerial drones illustrate the interest and relevance of the obtained results.

The aim of this diploma thesis is to analyse the advantages and disadvantages of different types of description of cyberphysical systems. It also concerns a description of the selected method that meets most of the current requirements for CPS design. In the practical part, attention is paid to the elaboration of an example for the specification of a selected cyberphysical system.

<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>

<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>

<p>Deep Learning (DL)-based edge intelligence has garnered significant attention in recent years due to the rapid proliferation of the Internet of Things (IoT), embedded, and intelligent systems, collectively termed edge devices. Sensor data streams acquired by these edge devices are processed by a Deep Neural Network (DNN) application that runs on the device itself or in the cloud. However, the high computational complexity and energy consumption of processing DNNs often limit their deployment on these edge inference systems due to limited compute, memory and energy resources. Furthermore, high costs, strict application latency demands, data privacy, security constraints, and the absence of reliable edge-cloud network connectivity heavily impact edge application efficiency in the case of cloud-assisted DNN inference. Inevitably, performance and energy efficiency are of utmost importance in these edge inference systems, aside from the accuracy of the application. To facilitate energy- efficient edge inference systems running computationally complex DNNs, this dissertation makes three key contributions.</p> <p><br></p> <p>The first contribution adopts a full-system approach to Approximate Computing, a design paradigm that trades off a small degradation in application quality for significant energy savings. Within this context, we present the foundational concepts of AxIS, the first approximate edge inference system that jointly optimizes the constituent subsystems leading to substantial energy benefits compared to optimization of the individual subsystem. To illustrate the efficacy of this approach, we demonstrate multiple versions of an approximate smart camera system that executes various DNN-based unimodal computer vision applications, showcasing how the sensor, memory, compute, and communication subsystems can all be synergistically approximated for energy-efficient edge inference.</p> <p><br></p> <p>Building on this foundation, the second contribution extends AxIS to multimodal AI, harnessing data from multiple sensor modalities to impart human-like cognitive and perceptual abilities to edge devices. By exploring optimization techniques for multiple sensor modalities and subsystems, this research reveals the impact of synergistic modality-aware optimizations on system-level accuracy-efficiency (AE) trade-offs, culminating in the introduction of SysteMMX, the first AE scalable cognitive system that allows efficient multimodal inference at the edge. To illustrate the practicality and effectiveness of this approach, we present an in-depth case study centered around a multimodal system that leverages RGB and Depth sensor modalities for image segmentation tasks.</p> <p><br></p> <p>The final contribution focuses on optimizing the performance of an edge-cloud collaborative inference system through intelligent DNN partitioning and computation offloading. We delve into the realm of distributed inference across edge devices and cloud servers, unveiling the challenges associated with finding the optimal partitioning point in DNNs for significant inference latency speedup. To address these challenges, we introduce PArtNNer, a platform-agnostic and adaptive DNN partitioning framework capable of dynamically adapting to changes in communication bandwidth and cloud server load. Unlike existing approaches, PArtNNer does not require pre-characterization of underlying edge computing platforms, making it a versatile and efficient solution for real-world edge-cloud scenarios.</p> <p><br></p> <p>Overall, this thesis provides novel insights, innovative techniques, and intelligent solutions to enable energy-efficient AI at the edge. The contributions presented herein serve as a solid foundation for future researchers to build upon, driving innovation and shaping the trajectory of research in edge AI.</p>

<p dir="ltr">The purpose of this qualitative case study was to describe how multi-cloud and cloud-based air gapped system security breaches occurred, how organizations responded, the kinds of data that were breached, and what security measures were implemented after the breach to prevent and repel future attacks. Qualitative research methods and secondary survey data were combined to answer the research questions. Due to the limited information available on successful unauthorized breaches to multi-cloud and cloud-based air gapped systems and corresponding data, the study was focused on the discovery of variables from several trustworthily sources of secondary data, including breach reports, press releases, public interviews, and news articles from the last five years and qualitative survey data. The sample included highly trained cloud professionals with air-gapped cloud experience from Amazon Web Services, Microsoft, Google and Oracle. The study utilized unstructured interviews with open-ended questions and observations to record and document data and analyze results.</p><p dir="ltr">By describing instances of multi-cloud and cloud-based air gapped system breaches in the last five years this study could add to the body of literature related to best practices for securing cloud-based data, preventing data breach on such systems, and for recovering from breach once it has occurred. This study would have significance to companies aiming to protect secure data from cyber attackers. It would also be significant to individuals who have provided their confidential data to companies who utilize such systems. In the primary data, 12 themes emerged. The themes were Air Gap Weaknesses Same as Other Systems, Misconfiguration of Cloud Settings, Insider Threat as Attack Vector, Phishing as Attack Vector, Software as Attack Vector, and Physical Media as Attack Vector, Lack of Reaction to Breaches, Better Authentication to Prevent Breaches, Communications, and Training in Response to Breach, Specific Responses to Specific Problems, Greater Separation of Risk from User End, and Greater Separation of Risk from Service End. For secondary data, AWS had four themes, Microsoft Azure had two, and both Google Cloud and Oracle had three.</p>

Von Beginn der flexiblen Automatisierung mit numerisch gesteuerten Werkzeugmaschinen und der zugehörigen Programmier-Software bis zum gegenwärtigen Entwicklungsstand (Industrie 4.0) wird die historische Entwicklung von 1960 bis 2020 aus der Position eines aktiven Mitgestalters dargestellt. Interessanterweise vollzogen sich die wesentlichen Entwicklungsetappen für die ersten dreißig Jahre parallel in beiden deutschen Staaten. Aus den Lehren des Zweiten Weltkrieges wurden im Rahmen der UNESCO zum friedlichen Informationsaustausch geeignete wissenschaftliche Organisationen gegründet: • IFIP (Internationale Föderation für Informationsprozesse, speziell Arbeitsgrupp CAM • CIRP (Internationale Akademie der Fertigungstechniker) Mit der Berufung und aktiven Mitarbeit in diesen Organisationen war eine Plattform für die deutsch-deutsche und darüber hinaus internationale Kooperation gegeben. Ein besonderer Schwerpunkt für den geordneten Informationsaustausch im Rahmen der gesamten dynamischen Entwicklung im Gebiet der Produktionsautomatisierung war dabei die im 3-Jahres-Rhythmus durchgeführte Konferenzserie PROLAMAT (Programming Languages for Machine Tools), gestartet 1969 in Rom. Im weiteren Verlauf wurde dieser Begriff viel breiter für das gesamte Gebiet der automatisierten Informationsverarbeitung und Fertigung erweitert. Ein besonderer Höhepunkt war dabei die erfolgreichste PROLAMAT-Konferenz 1988 in Dresden. Parallel dazu erfolgten an der TU Dresden Entwicklungen in Richtung CAD/CAM-Labor und später CIM-TT (CIM-Technologietransferzentrum). Damit war an der TU Dresden 1989/90 ein Entwicklungsstand gegeben, der unmittelbar zu gemeinsamen deutsch-deutschen und internationalen EU-Projekten genutzt werden konnte. Dieses hohe Entwicklungsniveau wurde zur offiziellen Eröffnung des CIM-TT-Zentrums in den Eröffnungsreferaten durch den damaligen Wissenschaftsminister Dr. Riesenhuber und Ministerpräsident Prof. Biedenkopf gewürdigt. Durch die zum gleichen Zeitpunkt verfügte veränderte Nutzung des für das CIM-TT im Aufbau befindliche Gebäude durch die neugegründete Juristische Fakultät wurde der erfolgreich vorbereitete Weg verhindert. Unabhängig davon blieb meine fachliche Orientierung mit den gravierenden Weiterentwicklungen eng verbunden. Dazu trug das Sabbatical-Jahr in Norwegen und den USA 1992 maßgeblich bei. Mit dem Forschungsaufenthalt war die Entscheidungsvorbereitung für die vorgesehene Groß-Investition für das neueste generative Verfahren verbunden. Gleichzeitig mit dem fundierten Nachweis der bestgeeigneten sog. Rapid-Prototyping-Anlage vom deutschen Anbieter EOS München war die TU Dresden auf diesem neuen High-Tech-Gebiet 1992 in einer anerkannten Spitzenposition. Mit meiner Publikation eines der ersten Fachbücher im Gebiet Advanced Prototyping (jetzt Additiv Manufacturing) war darüber hinaus eine gute Basis für weitere innovative Aktivitäten gegeben Dazu gehört die Gründung einer High-Tech-Firma (SFM - Schnelle Fertigung von Modellen) mit bemerkenswerten beispielgebenden Ergebnissen. Hervorgehoben soll die zwanzigjährige aktive Kooperation mit der Universität Stellenbosch (RSA - Republik Südafrika), die unter anderem mit meiner Berufung zum Extraordinary Professor im Jahr 2003 verbunden ist. Mit der Eröffnung eines Technologie-Zentrums nach dem Vorbild des ursprünglichen CIM TT -Zentrums der TU Dresden konnte für Südafrika ein wertvoller Beitrag geleistet werden. Das gesamte Lebenswerk ist gekennzeichnet durch die Entwicklungsschritte von der Mathematisierung über die Algorithmierung bis hin zur Programmierung vielfältiger technologischer Sachverhalte. Die Ergebnisse sind in einer Anzahl von persönlichen Fachbüchern (z.T. übersetzt in das Russische und Ungarische) wie auch Konferenzberichten und mehr als 200 Veröffentlichungen (deutsch und englisch) dokumentiert.

<p>The "Maker Movement" is a cultural phenomena rooted in DIY culture, which stresses making devices and creations on your own rather than purchasing it ready-made. At the core of the Maker Movement, is the "Maker Mindset"; a collection of attitudes, beliefs and behaviors that emphasize the importance of creativity, experimentation and innovation in the learning process. Since the Maker Mindset embodies constructionist principles at its core that push makers to experiment and problem-solve by collaborating with fellow makers through hands-on activities, it can be said that these activities comprise of Creative, Learning and Collaborative experiences. While Internet-of-Things devices have long been used to enhance these activities, research pertaining to using Augmented Reality in tandem with IoT for the purpose of enhancing experiences core to the Maker Mindset is relatively unexplored. Three different systems were developed with the goal of addressing this -- MicrokARts, ShARed IoT and MechARspace. Each system focuses on enhancing one of the three core experiences through AR-compatible IoT devices, whilst ensuring that they do not require prerequisite knowledge in order to author AR experiences. These systems were evaluated through user studies and testing over a variety of age-groups, with each system successfully enhancing one core experience each through the use of AR-IoT interactions.</p>