SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted Machine Learning (ML) Technologies(John Jay College of Criminal Justice, 2024-04-03) Alanazi, Mosa; Seferaj, GentianaIntegrating Machine Learning (ML) technologies into physical security has ignited significant discourse within scholarly circles, focusing on identifying specific ML technologies currently employed and elucidating their tangible outcomes. This integration occurs against a rapidly evolving technological landscape, encompassing advancements such as cloud computing, 5G wireless technology, real-time Internet of Things (IoT) data, surveillance cameras fortified with biometric technologies, and predictive data analytics. Collectively, these innovations augment the transformative potential of ML within security frameworks, ranging from sophisticated video analytics facilitating advanced threat detection to predictive algorithms aiding in comprehensive risk assessment. Moreover, the seamless fusion of disparate data streams and the capability to extract actionable insights in real-time present profound implications for the future trajectory of security protocols, heralding a paradigm shift in the conceptualization, implementation, and Student No: 10001 Page 2 of 14 Comprehensive Exam/Project ̶̶̶ Spring24 Department of Security, Fire and Emergency Management maintenance of physical security measures. This study endeavors to delve into the specifics of ML technologies currently operationalized in physical security contexts, scrutinize the tangible outcomes they yield, and forecast how these trends will shape the future security landscape— additionally, strategic recommendations aimed at optimizing the efficacy of ML-driven security solutions in safeguarding physical environments.133 0Item Restricted Physics and AI-Driven Anomaly Detection in Cyber-Physical Systems(Saudi Digital Library, 2023) Alotibi, Faris; Tipper, DavidOrganizations across various sectors are moving rapidly to digitization. Multiple applications in cyber-physical systems (CPSs) emerged from interconnectivity such as smart cities, autonomous vehicles, and smart grids, utilizing advanced capabilities of the Internet of Things (IoTs), cloud computing, and machine learning. Interconnectivity also becomes a critical component in industrial systems such as smart manufacturing, smart oil, and gas distribution grid, smart electric power grid, etc. These critical infrastructures and systems rely on industrial IoT and learning-enabled components to handle the uncertainty and variability of the environment and increase autonomy in making effective operational decisions. The prosperity and benefits of systems interconnectivity demand the fulfillment of functional requirements such as interoperability of communication and technology, efficiency and reliability, and real-time communication. Systems need to integrate with various communication technologies and standards, process and analyze shared data efficiently, ensure the integrity and accuracy of exchanged data, and execute their processes with tolerable delay. This creates new attack vectors targeting both physical and cyber components. Protection of systems interconnection and validation of communicated data against cyber and physical attacks become critical due to the consequences of disruption attacks pose to critical systems. In this dissertation, we tackle one of the prominent attacks in the CPS space, namely the false data injection attack (FDIA). FDIA is an attack executed to maliciously influence decisions, that is CPSs operational decisions such as opening a valve, changing wind turbine configurations, charging/discharging energy storage system batteries, or coordinating autonomous vehicles driving. We focus on the development of anomaly detection techniques to protect CPSs from this emerging threat. The anomaly detection mechanisms leverage both physics of CPSs and AI to improve their detection capability as well as the CPSs' ability to mitigate the impact of FDIA on their operations.51 0