Seferaj, GentianaAlanazi, Mosa2024-06-062024-06-062024-04-0310001https://hdl.handle.net/20.500.14154/72262Integrating 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.16en-USMachine LearningPhysical SecurityFacial RecognitionAnomaly DetectionCloud Computing5G Wireless TechnologyInternet of Things (IoT)Biometric SurveillancePredictive AnalyticsAlgorithmic BiasPrivacy ConcernsThreat DetectionRisk ManagementSecurity MeasuresFuture TrendsThesis