The rapid expansion of satellite networks has enabled the widespread deployment of satellite-based services across various sectors. However, these networks often prioritize cost-effectiveness over security considerations, leading to inherent architectural vulnerabilities. The complex architecture, comprising heterogeneous devices, operating systems, and communication protocols, exacerbates security risks and broadens the attack surface. Attackers can exploit these threat vectors to compromise system availability and data integrity. Prior research has primarily focused on specific security improvements, providing limited comprehensive evaluations and systematic threat modeling. This study proposes a systematic approach for modeling and analyzing satellite network security. A security framework originally developed for traditional networks has been adapted for satellite environments. Utilizing the Threat Vector–Hierarchical Attack Representation Model (TV-HARM), this study comprehensively models and analyzes threat vectors and network security. Key attack paths are identified, and vulnerabilities are quantitatively assessed using three refined security metrics. The experimental results reveal residual threats despite existing security measures, underscoring the need for robust defense strategies. This study presents a systematic framework for evaluating satellite network security, demonstrating the applicability and effectiveness of the proposed methodologies. These findings contribute to enhanced threat mitigation strategies and the overall improvement of satellite network security by addressing critical vulnerabilities.

Cloud computing has been adopted widely, providing on-demand computing resources to improve perfornance and reduce the operational costs. However, these new functionalities also bring new ways to exploit the cloud computing environment. To assess the security of the cloud, graphical security models can be used, such as Attack Graphs and Attack Trees. However, existing models do not consider all types of threats, and also automating the security assessment functions are difficult. In this paper, we propose a new security assessment tool for the cloud named CloudSafe, an automated security assessment for the cloud. The CloudSafe tool collates various tools and frameworks to automate the security assessment process. To demonstrate the applicability of the CloudSafe, we conducted security assessment in Amazon AWS, where our experimental results showed that we can effectively gather security information of the cloud and carry out security assessment to produce security reports. Users and cloud service providers can use the security report generated by the CloudSafe to understand the security posture of the cloud being used/provided.