I came, I saw, I certified: some perspectives on the safety assurance of cyber-physical systems

• URL: http://arxiv.org/abs/2401.16633v1
• Date: Tue, 30 Jan 2024 00:06:16 GMT
• Title: I came, I saw, I certified: some perspectives on the safety assurance of cyber-physical systems
• Authors: Mithila Sivakumar, Alvine B. Belle, Kimya Khakzad Shahandashti, Oluwafemi Odu, Hadi Hemmati, Segla Kpodjedo, Song Wang, Opeyemi O. Adesina
• Abstract summary: Execution failure of cyber-physical systems could result in loss of life, severe injuries, large-scale environmental damage, property destruction, and major economic loss. It is often mandatory to develop compelling assurance cases to support that justification and allow regulatory bodies to certify such systems. We explore challenges related to such assurance enablers and outline some potential directions that could be explored to tackle them.
• Score: 5.9395940943056384
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The execution failure of cyber-physical systems (e.g., autonomous driving systems, unmanned aerial systems, and robotic systems) could result in the loss of life, severe injuries, large-scale environmental damage, property destruction, and major economic loss. Hence, such systems usually require a strong justification that they will effectively support critical requirements (e.g., safety, security, and reliability) for which they were designed. Thus, it is often mandatory to develop compelling assurance cases to support that justification and allow regulatory bodies to certify such systems. In such contexts, detecting assurance deficits, relying on patterns to improve the structure of assurance cases, improving existing assurance case notations, and (semi-)automating the generation of assurance cases are key to develop compelling assurance cases and foster consumer acceptance. We therefore explore challenges related to such assurance enablers and outline some potential directions that could be explored to tackle them.

Related papers

• Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
  We propose Authenticated Cyclic Redundancy Integrity Check (ACRIC) ACRIC preserves backward compatibility without requiring additional hardware and is protocol agnostic. We show that ACRIC offers robust security with minimal transmission overhead ( 1 ms)
  arXiv  Detail & Related papers  (2024-11-21T18:26:05Z)
• Automating Semantic Analysis of System Assurance Cases using Goal-directed ASP [1.2189422792863451]
  We present our approach to enhancing Assurance 2.0 with semantic rule-based analysis capabilities. We examine the unique semantic aspects of assurance cases, such as logical consistency, adequacy, indefeasibility, etc.
  arXiv  Detail & Related papers  (2024-08-21T15:22:43Z)
• A PRISMA-Driven Bibliometric Analysis of the Scientific Literature on Assurance Case Patterns [7.930875992631788]
  Assurance cases can be used to prevent system failure. They are structured arguments that allow arguing and relaying various safety-critical systems' requirements.
  arXiv  Detail & Related papers  (2024-07-06T05:00:49Z)
• Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
  We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
  arXiv  Detail & Related papers  (2024-05-10T17:38:32Z)
• ACCESS: Assurance Case Centric Engineering of Safety-critical Systems [9.388301205192082]
  Assurance cases are used to communicate and assess confidence in critical system properties such as safety and security. In recent years, model-based system assurance approaches have gained popularity to improve the efficiency and quality of system assurance activities. We show how model-based system assurance cases can trace to heterogeneous engineering artifacts.
  arXiv  Detail & Related papers  (2024-03-22T14:29:50Z)
• Dynamic Risk Management in Cyber Physical Systems [1.7687476868741456]
  This paper structures safety assurance challenges of cooperative automated CPS. It provides an overview on our vision of dynamic risk management and describes already existing building blocks.
  arXiv  Detail & Related papers  (2024-01-24T15:50:32Z)
• The Last Decade in Review: Tracing the Evolution of Safety Assurance Cases through a Comprehensive Bibliometric Analysis [7.431812376079826]
  Safety assurance is of paramount importance across various domains, including automotive, aerospace, and nuclear energy. The use of safety assurance cases allows for verifying the correctness of the created systems capabilities, preventing system failure.
  arXiv  Detail & Related papers  (2023-11-13T17:34:23Z)
• 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)
• Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions [60.26921219698514]
  We introduce a model-uncertainty-aware reformulation of CBF-based safety-critical controllers. We then present the pointwise feasibility conditions of the resulting safety controller. We use these conditions to devise an event-triggered online data collection strategy.
  arXiv  Detail & Related papers  (2022-08-23T05:02:09Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)
• Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims [59.64274607533249]
  AI developers need to make verifiable claims to which they can be held accountable. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
  arXiv  Detail & Related papers  (2020-04-15T17:15:35Z)

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.