This study, conducted with the cooperation of the long-established Nishikawa Construction Company in Mie Prefecture, Japan, explores the possibility of transmitting centuries-old architectural traditions from human to artificial intelligence. Utilizing sensors and data collection methods to gather human behavior, speech, vital data, and decision-making processes, we propose a framework to distill the architectural philosophy and creativity of the daimyo architects of the Sengoku period into a digital format, thereby applying it to contemporary architectural practice. Specifically, we juxtapose the enduring influence of historical constructions such as Himeji Castle and Nijo Castle with the modern construction methodologies employed by Nishikawa Construction Company, focusing on the utilization of data to enhance the precision and robustness of AI. This research demonstrates that AI can be a potent tool in preserving and utilizing the knowledge and experience of past masters, resulting in a convergence of enduring architectural principles with innovative AI applications. It reveals a symbiotic existence of historical wisdom and technological advancement, suggesting the potential for modern architects and builders to make a more significant societal impact through enhanced design and construction integrity.

This research proposes a new malware detection technique using a multi-dimensional evaluation function, which comprehensively assesses multiple indicators such as power consumption, network traffic, and CPU usage. This method enables the rapid scoring of malware risks and prioritizes the detection of malware that presents a risk score above a certain threshold. Moreover, this technique surpasses the detection speed of existing security software and provides flexibility by utilizing machine learning to adapt to new patterns and behaviors of malware. These characteristics are expected to significantly improve the efficiency and accuracy of malware detection.

In the current digital landscape, there has been a surge in instances where external entities access information devices, process data on them, and retrieve only the results. While current security measures are designed with the assumption that computer power is sufficiently available, there is a growing need for an integrated security design methodology that takes into consideration scenarios where computer power is limited or rapidly diminishing. Notably, in scenarios where the PC's power source is a battery or in cases of drones and unmanned aerial vehicles, the power supply becomes a paramount resource due to its limitations. Unauthorized consumption of these power resources is tantamount to malware from an intruder's viewpoint. Central to this research is the exploration and discussion of such Integrated Security Design Methods that address this power-related vulnerability. Our objective is to delineate system configurations specifically tailored to safeguard power resources in setups that don't anticipate external power supplies and emphasize the pressing need for these advanced security measures.

With the recent advancements in autonomous vehicle technology, there has been a significant surge in the utilization of onboard sensor data. However, concerns regarding sensor anomalies and security issues have concurrently intensified. Malfunctions in sensors can lead to the dissemination of inaccurate data and potential vehicular malfunctions, heightening vulnerabilities in security. This poses a risk to the safety of both drivers and pedestrians and may adversely impact the trustworthiness and adoption rate of autonomous driving technology. In this study, we introduce a novel anomaly detection algorithm, grounded in the operational principles of onboard sensors and surrounding environmental conditions. This algorithm contrasts the actual measurements from the sensors with theoretical estimations to detect anomalies. As a criterion for anomaly detection, instances where the disparity between the estimated and actual values surpasses a specific threshold are identified, with the threshold optimized using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC). This method aims to achieve high true positive and true negative rates, bolstering the security and reliability of sensors in autonomous vehicles. Additionally, it facilitates performance comparisons across different vehicle types and environments.