컨테이너 시스템의 보안 위협 파악: 잘못 설정된 컨테이너 구성 요소에 대한 연구

학위논문(석사) - 한국과학기술원 : 정보보호대학원, 2023.8,[iii, 26 p. :]The increasing popularity of cloud computing has led to a significant rise in the usage of container technology. Docker and Kubernetes have emerged as standard tools for managing container systems due to their reliability and flexibility. However, REST APIs on container systems have introduced additional attack vectors. In this paper, we investigate the security threats posed by misconfigured container components that are exposed to the Internet. Our study involves an Internet-scale measurement to assess their prevalence. For this end, we collect a total of 1,003,947 IP addresses and identify renowned institutes, governments, and enterprises which are operating exposed and misconfigured container components, indicating potential compromises. Additionally, we conduct a real-world experiment within multi-branch institutes to identify vulnerabilities in misconfigured container components. By conducting a comprehensive scan of all 150,235 IP addresses associated with an institute, we identify three vulnerable servers among the 57 misconfigured container components. This finding demonstrates the feasibility and profitability of exploiting these containers through targeted attacks. This comprehensive investigation provides insights into the prevalence of misconfigured container components to safeguard container systems from unauthorized access. By addressing these security concerns, organizations can protect the integrity and confidentiality of their containerized environments more effectively.한국과학기술원 :정보보호대학원

Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation

The purpose of this study was to establish a high-performing radiomics strategy with machine learning from conventional and diffusion MRI to differentiate recurrent glioblastoma (GBM) from radiation necrosis (RN) after concurrent chemoradiotherapy (CCRT) or radiotherapy. Eighty-six patients with GBM were enrolled in the training set after they underwent CCRT or radiotherapy and presented with new or enlarging contrast enhancement within the radiation field on follow-up MRI. A diagnosis was established either pathologically or clinicoradiologically (63 recurrent GBM and 23 RN). Another 41 patients (23 recurrent GBM and 18 RN) from a different institution were enrolled in the test set. Conventional MRI sequences (T2-weighted and postcontrast T1-weighted images) and ADC were analyzed to extract 263 radiomic features. After feature selection, various machine learning models with oversampling methods were trained with combinations of MRI sequences and subsequently validated in the test set. In the independent test set, the model using ADC sequence showed the best diagnostic performance, with an AUC, accuracy, sensitivity, specificity of 0.80, 78%, 66.7%, and 87%, respectively. In conclusion, the radiomics models models using other MRI sequences showed AUCs ranging from 0.65 to 0.66 in the test set. The diffusion radiomics may be helpful in differentiating recurrent GBM from RN.