Open Platform-Based File System Forensics

Author(s)
이진오
Advisor
손태식
Department
일반대학원 AI융합네트워크학과
Publisher
The Graduate School, Ajou University
Publication Year
2023-02
Language
eng
Keyword
Digital ForensicIoTMetadatafile system
Alternative Abstract
Currently, various devices that are combined with IoT technology are being developed, and research on the development of devices continues. These IoT devices occupy many parts of our lives, such as smart phones, vehicles, and industries. However, as IoT devices are rapidly spreading, new attacks are occurring against them. In particular, due to the environmental characteristics in which various devices are connected to the Internet, great damage may occur to the user in the event of an attack. In addition, research on devices is essential because data stored on IoT devices can be important evidence and alibi in the event of a crime. However, due to the nature of IoT devices, there are many small products, and it is difficult to extract data through general methods due to the installation of various platforms. To solve these problems, research on improving the security of IoT devices and obtaining data is underway, but constant research is required because the types of IoT devices are very diverse and new products are continuously released. In addition, research is needed to obtain more data and contribute to security improvement through analysis at the file system stage on the device, not just the analysis of the device. <br>Therefore, in this study, analysis is conducted on various platforms used in IoT devices. The platforms used in the study were Tizen, Linux, and AAOS, and the platforms installed in IoT used in various fields were selected. In this study, it is possible to improve the security of the platform by identifying the file system used for each platform and analyzing metadata. We also propose a data extraction plan based on the analyzed metadata, and analyze the recoverability of the deleted data.
URI
https://dspace.ajou.ac.kr/handle/2018.oak/24591
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Graduate School of Ajou University > Department of Artificial Intelligence Convergence Network > 3. Theses(Master)
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