Joonhyuk Park (School of Cybersecurity, Korea University), Jiwon Kwak (School of Cybersecurity, Korea University), Geunwoo Baek (School of Cybersecurity, Korea University), Dohee Kang (School of Cybersecurity, Korea University), Seungjoo Kim (School of Cybersecurity, Korea University)

The increasing significance of space-system cybersecurity in the space industry underscores the necessity of moving beyond development paradigms based on security by obscurity. Consequently, international standards such as ISO 20517 recommend the use of threat modeling to ensure security when developing space systems. Because manual threat modeling is time-consuming, it has motivated substantial research into the development of automated tools. Despite this interest, automated threat modeling tools specialized for the space domain remain scarce. Therefore, this paper proposes an automated threat modeling tool for the space domain by enhancing the Microsoft Threat Modeling Tool (MS-TMT). The tool was developed by integrating the Aerospace SPARTA matrix and the D3FEND knowledge base into MS-TMT. To evaluate its effectiveness, we conducted a case study involving four space-system security incidents, including the Viasat hacking. In the absence of existing satellite-specific threat modeling tools, we selected SecOpsTM as a comparative baseline because it is an automated threat modeling tool that identifies threats in a manner conceptually similar to our approach, enabling a fair and meaningful comparison. The quantitative evaluation demonstrated that our tool achieved an accuracy of 100%, whereas SecOpsTM achieved an average accuracy of 54%.

View More Papers

PhyFuzz: Detecting Sensor Vulnerabilities with Physical Signal Fuzzing

Zhicong Zheng (Zhejiang University), Jinghui Wu (Zhejiang University), Shilin Xiao (Zhejiang University), Yanze Ren (Zhejiang University), Chen Yan (Zhejiang University), Xiaoyu Ji (Zhejiang University), Wenyuan Xu (Zhejiang University)

Read More

CatBack: Universal Backdoor Attacks on Tabular Data via Categorical...

Behrad Tajalli (Radboud University), Stefanos Koffas (Delft University of Technology), Stjepan Picek (Radboud University)

Read More

Dataset Reduction and Watermark Removal via Self-supervised Learning for...

Hao Luan (Institute of Big Data, Fudan University, Shanghai, China and College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China), Xue Tan (Institute of Big Data, Fudan University, Shanghai, China and College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China), Zhiheng Li (School of Control Science and Engineering, Shandong University, Jinan,…

Read More