Safety and Security Risk Mitigation in Satellite Missions via Attack-Fault-Defense Trees

• URL: http://arxiv.org/abs/2504.00988v1
• Date: Tue, 01 Apr 2025 17:24:43 GMT
• Title: Safety and Security Risk Mitigation in Satellite Missions via Attack-Fault-Defense Trees
• Authors: Reza Soltani, Pablo Diale, Milan Lopuhaä-Zwakenberg, Mariëlle Stoelinga,
• Abstract summary: This work presents a case study from Ascentio Technologies, a mission-critical system company in Argentina specializing in aerospace.<n>The main focus will be on the Ground Segment for the satellite project currently developed by the company.<n>This paper showcases the application of the Attack-Fault-Defense Tree framework, which integrates attack trees, fault trees, and defense mechanisms into a unified model.
• Score: 2.252059459291148
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cyber-physical systems, such as self-driving cars or digitized electrical grids, often involve complex interactions between security, safety, and defense. Proper risk management strategies must account for these three critical domains and their interaction because the failure to address one domain can exacerbate risks in the others, leading to cascading effects that compromise the overall system resilience. This work presents a case study from Ascentio Technologies, a mission-critical system company in Argentina specializing in aerospace, where the interplay between safety, security, and defenses is critical for ensuring the resilience and reliability of their systems. The main focus will be on the Ground Segment for the satellite project currently developed by the company. Analyzing safety, security, and defense mechanisms together in the Ground Segment of a satellite project is crucial because these domains are deeply interconnected--for instance, a security breach could disable critical safety functions, or a safety failure could create opportunities for attackers to exploit vulnerabilities, amplifying the risks to the entire system. This paper showcases the application of the Attack-Fault-Defense Tree (AFDT) framework, which integrates attack trees, fault trees, and defense mechanisms into a unified model. AFDT provides an intuitive visual language that facilitates interdisciplinary collaboration, enabling experts from various fields to better assess system vulnerabilities and defenses. By applying AFDT to the Ground Segment of the satellite project, we demonstrate how qualitative analyses can be performed to identify weaknesses and enhance the overall system's security and safety. This case highlights the importance of jointly analyzing attacks, faults, and defenses to improve resilience in complex cyber-physical environments.

Related papers

• Secure Physical Layer Communications for Low-Altitude Economy Networking: A Survey [76.36166980302478]
  The Low-Altitude Economy Networking (LAENet) is emerging as a transformative paradigm. Physical layer communications in the LAENet face growing security threats due to inherent characteristics of aerial communication environments. This survey comprehensively reviews existing secure countermeasures for physical layer communication in the LAENet.
  arXiv  Detail & Related papers  (2025-04-12T09:36:53Z)
• An Approach to Technical AGI Safety and Security [72.83728459135101]
  We develop an approach to address the risk of harms consequential enough to significantly harm humanity.<n>We focus on technical approaches to misuse and misalignment.<n>We briefly outline how these ingredients could be combined to produce safety cases for AGI systems.
  arXiv  Detail & Related papers  (2025-04-02T15:59:31Z)
• Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks [22.154001025679896]
  Embodied AI systems, including robots and autonomous vehicles, are increasingly integrated into real-world applications.<n>These vulnerabilities manifest through sensor spoofing, adversarial attacks, and failures in task and motion planning.
  arXiv  Detail & Related papers  (2025-02-18T03:38:07Z)
• Open Problems in Machine Unlearning for AI Safety [61.43515658834902]
  Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks.<n>In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety.
  arXiv  Detail & Related papers  (2025-01-09T03:59:10Z)
• Considerations Influencing Offense-Defense Dynamics From Artificial Intelligence [0.0]
  AI can enhance defensive capabilities but also presents avenues for malicious exploitation and large-scale societal harm.<n>This paper proposes a taxonomy to map and examine the key factors that influence whether AI systems predominantly pose threats or offer protective benefits to society.
  arXiv  Detail & Related papers  (2024-12-05T10:05:53Z)
• Defining and Evaluating Physical Safety for Large Language Models [62.4971588282174]
  Large Language Models (LLMs) are increasingly used to control robotic systems such as drones. Their risks of causing physical threats and harm in real-world applications remain unexplored. We classify the physical safety risks of drones into four categories: (1) human-targeted threats, (2) object-targeted threats, (3) infrastructure attacks, and (4) regulatory violations.
  arXiv  Detail & Related papers  (2024-11-04T17:41:25Z)
• Enhancing Enterprise Security with Zero Trust Architecture [0.0]
  Zero Trust Architecture (ZTA) represents a transformative approach to modern cybersecurity. ZTA shifts the security paradigm by assuming that no user, device, or system can be trusted by default. This paper explores the key components of ZTA, such as identity and access management (IAM), micro-segmentation, continuous monitoring, and behavioral analytics.
  arXiv  Detail & Related papers  (2024-10-23T21:53:16Z)
• Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI [52.138044013005]
  generative AI, particularly large language models (LLMs), become increasingly integrated into production applications. New attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems. Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks. This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.
  arXiv  Detail & Related papers  (2024-09-23T10:18:10Z)
• The MESA Security Model 2.0: A Dynamic Framework for Mitigating Stealth Data Exfiltration [0.0]
  Stealth Data Exfiltration is a significant cyber threat characterized by covert infiltration, extended undetectability, and unauthorized dissemination of confidential data. Our findings reveal that conventional defense-in-depth strategies often fall short in combating these sophisticated threats. As we navigate this complex landscape, it is crucial to anticipate potential threats and continually update our defenses.
  arXiv  Detail & Related papers  (2024-05-17T16:14:45Z)
• Leveraging Traceability to Integrate Safety Analysis Artifacts into the Software Development Process [51.42800587382228]
  Safety assurance cases (SACs) can be challenging to maintain during system evolution. We propose a solution that leverages software traceability to connect relevant system artifacts to safety analysis models. We elicit design rationales for system changes to help safety stakeholders analyze the impact of system changes on safety.
  arXiv  Detail & Related papers  (2023-07-14T16:03:27Z)
• Synergistic Redundancy: Towards Verifiable Safety for Autonomous Vehicles [10.277825331268179]
  We propose Synergistic Redundancy (SR) a safety architecture for complex cyber physical systems, like Autonomous Vehicle (AV) SR provides verifiable safety guarantees against specific faults by decoupling the mission and safety tasks of the system. Close coordination with the mission layer allows easier and early detection of safety critical faults in the system.
  arXiv  Detail & Related papers  (2022-09-04T23:52:03Z)
• Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks [62.923992740383966]
  We present the first study of security issues of MSF-based perception in AD systems. We generate a physically-realizable, adversarial 3D-printed object that misleads an AD system to fail in detecting it and thus crash into it. Our results show that the attack achieves over 90% success rate across different object types and MSF.
  arXiv  Detail & Related papers  (2021-06-17T05:11:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.