2022, Vol.27, No.6

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

pISSN : 1598-849X / eISSN : 2383-9945
https://journal.kci.go.kr/jksci

In this paper, we propose a general-purpose fault injection system for Linux loadable kernel modules. The fault injection system enables software developers and testers to inject various kinds of faults easily into user-specified kernel modules in user-controlled manner. The proposed system also provides workload generation in order to make injected faults be exposed effectively, By experiments, we show that the fault injection system correctly injects faults into Linux kernel modules. The proposed system can be utilized as a useful tool for testing during kernel module development It is also useful for studies on kernel behaviour analysis and fault isolation and recovery.

Although interest in de-identification measures for the safe use of personal information is growing at home and abroad, cases where de-identified information is re-identified through insufficient de-identification measures and inferences are occurring. In order to compensate for these problems and discover new technologies for de-identification measures, competitions to compete on the safety and usefulness of de-identified information are being held in Korea and Japan. This paper analyzes the safety and usefulness indicators used in these competitions, and proposes and verifies new indicators that can measure usefulness more efficiently. Although it was not possible to verify through a large population due to a significant shortage of experts in the fields of mathematics and statistics in the field of de-identification processing, very positive results could be derived for the necessity and validity of new indicators. In order to safely utilize the vast amount of public data in Korea as de-identified information, research on these usefulness metrics should be continuously conducted, and it is expected that more active research will proceed starting with this thesis.

Due to the recent outbreak of COVID-19 and an aging population and an increase in single-person households, the amount of time that household members spend doing various activities at home has increased significantly. In this study, we propose an algorithm for detecting anomalies in members of single-person households, including the elderly, based on the results of human movement and fall detection using an image sensor algorithm through home CCTV, an activity sensor algorithm using an acceleration sensor built into a smartphone, and a 2D LiDAR sensor-based LiDAR sensor algorithm. However, each single sensor-based algorithm has a disadvantage in that it is difficult to detect anomalies in a specific situation due to the limitations of the sensor. Accordingly, rather than using only a single sensor-based algorithm, we developed a fusion method that combines each algorithm to detect anomalies in various situations. We evaluated the performance of algorithms through the data collected by each sensor, and show that even in situations where only one algorithm cannot be used to detect accurate anomaly event through certain scenarios we can complement each other to efficiently detect accurate anomaly event.