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>

<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>

<p>From idea generation to conceptualization and development of products and technologies is a non-linear and iterative process. The work in this thesis follows a process that initiates with the review of existing technologies and products, examining their unique value proposition in the context of the specific applications for which they are designed. Next, the unmet needs of novel or emerging applications are identified that require new product or technologies. Once these user needs and product requirements are identified, the specific functions to be addressed by the product are specified. The subsequent process of design of products and technologies to meet these functions is enabled by engineering tools such as three-dimensional modelling, physics-based simulations, and manufacturing of a minimum viable prototype. In these steps, un-biased decisions have to be taken using weighted decision matrices to cater to the design requirements. Finally, the minimum viable prototype is tested to demonstrate the principal functionalities. The results obtained from the testing process identify the potential future improvements in the next generations of the prototype that would subsequently inform the final design of product. This thesis adopted this methodology to initiate the design two product-prototypes: i) an image-recognition-integrated service (IRIS) robotic hand for children and ii) cascaded multi-core vapor chamber (CMVC) for improving performance of next-generation computing systems. Minimum viable product-prototypes were manufactured to demonstrate the principal functionalities, followed by clear identification of future potential improvements. Tests of the prosthetic hand indicate that the image-recognition based feedback can successfully drive the actuators to perform the intended grasping motions. Experimental testing with the multi-core vapor chamber demonstrates successful performance of the prototype, which offers notable reduction in temperatures relative to the existing benchmark solid copper spreader. </p>