As the development of technology accelerates, the Fourth Industrial Revolution, which combines various technologies and provides them as one service, has been in the spotlight, and services using big data, Artificial Intelligence (AI) and Internet of Things (IoT) are becoming more intelligent and helpful to users. As these services are used in various fields, attacks by attackers also occur in various areas and ways. However, cyberattacks by attackers may vary depending on the attacking pattern of the attacker, and the same vulnerability can be attacked from different perspectives. Therefore, in this study, by constructing a cyberattack framework based on preemptive prediction, we can collect vulnerability information based on big data existing on the network and increase the accuracy by applying machine learning to the mapping of keywords frequently mentioned in attack strategies. We propose an attack strategy prediction framework.

A significant number of cloud storage environments are already implementing deduplication technology. Due to the nature of the cloud environment, a storage server capable of accommodating large-capacity storage is required. As storage capacity increases, additional storage solutions are required. By leveraging deduplication, you can fundamentally solve the cost problem. However, deduplication poses privacy concerns due to the structure itself. In this paper, we point out the privacy infringement problem and propose a new deduplication technique to solve it. In the proposed technique, since the user's map structure and files are not stored on the server, the file uploader list cannot be obtained through the server's meta-information analysis, so the user's privacy is maintained. In addition, the personal identification number (PIN) can be used to solve the file ownership problem and provides advantages such as safety against insider breaches and sniffing attacks. The proposed mechanism required an additional time of approximately 100 ms to add a ID Ref to distinguish user-file during typical deduplication, and for smaller file sizes, the time required for additional operations is similar to the operation time, but relatively less time as the file's capacity grows.

This study reviews the structure of wooden printing blocks in Japan, focusing on the blocks as threedimensional objects. Inspection is more effective three-dimensionally than two-dimensionally, and for the first time in wooden printing block research, the study uses a 3D CT scanner and a high-resolution 3D digitizer. The 3D CT scanner examines cross sections of the blocks and identifies their grain and contents, including insects surviving within them. The 3D digitizer enables observation of objects up to 0.02 mm; this allows detailed collection of block surface information, which is difficult to identify with a conventional microscope.