Opportunities and Challenges in Applying Machine Learning for Access Control

Access control systems are important in regulating who can access physical facilities, computer systems, and data resources. However, traditional access control systems have limitations in adapting to changing user behavior and evolving security threats. We argue that applying machine learning is a promising direction to access control systems that can enhance their accuracy, efficiency, and effectiveness. Machine learning can help identify and mitigate security risks in real time by detecting patterns of suspicious activity that may indicate a security breach or attempted attack. It can also learn to identify anomalies and raise alerts, enabling security personnel to respond quickly and prevent potential security incidents

Human-Centric Enterprise Security: Advancing Access Control through AI-Driven Administration

This paper explores the integration of Artificial Intelligence (AI) in access control to enhance enterprise security, focusing on the synergy between human administra- tors and intelligent systems. It begins by addressing challenges faced by human administrators and emphasizes the role of Explainable AI (XAI) in ensuring trans- parency. Case studies illustrate the effectiveness of AI in automating policy updates while involving human oversight. The paper concludes by discussing po- tential advancements and challenges, highlighting the pivotal collaboration between humans and AI in achieving adaptive and robust enterprise security

Human-Centric Enterprise Security: Advancing Access Control through AI-Driven Administration

This paper explores the integration of Artificial Intelligence (AI) in access control to enhance enterprise security, focusing on the synergy between human administrators and intelligent systems. It begins by addressing challenges faced by human administrators and emphasizes the role of Explainable AI (XAI) in ensuring transparency. Case studies illustrate the effectiveness of AI in automating policy updates while involving human oversight. The paper concludes by discussing potential advancements and challenges, highlighting the pivotal collaboration between humans and AI in achieving adaptive and robust enterprise security

Significant THz-wave absorption property in mixed δ- and α-FAPbI3 hybrid perovskite flexible thin film formed by sequential vacuum evaporation

The flexible and mixed delta- and alpha-formamidinium lead iodide (FAPbI(3)) hybrid perovskite thin films fabricated by the sequential vacuum evaporation method (SVE) were studied using the THz-wave absorption property. The formed FAPbI(3) using SVE showed the typical delta-phase. After annealing for 10 min, we confirmed a mixed state with delta- and alpha-phases and observed THz-wave absorption at 1.62 THz with 40% absorptance. In the C 1s core-level spectra, we found two different chemical states originated from delta- and alpha-FAPbI(3). The origin of the THz-wave absorption property is assumed from a significant Pb-I vibration mode from the mixed phases in FAPbI(3). (C) 2019 The Japan Society of Applied Physic