A Practical Approach to Formal Methods: An Eclipse Integrated Development Environment (IDE) for Security Protocols

• URL: http://arxiv.org/abs/2411.17926v1
• Date: Tue, 26 Nov 2024 22:44:08 GMT
• Title: A Practical Approach to Formal Methods: An Eclipse Integrated Development Environment (IDE) for Security Protocols
• Authors: Rémi Garcia, Paolo Modesti,
• Abstract summary: We present an Eclipse IDE for the design, verification, and implementation of security protocols.<n>It offers user-friendly assistance in the formalisation process as part of a Model-Driven Development approach.
• Score: 0.33148826359547523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To develop trustworthy distributed systems, verification techniques and formal methods, including lightweight and practical approaches, have been employed to certify the design or implementation of security protocols. Lightweight formal methods offer a more accessible alternative to traditional fully formalised techniques by focusing on simplified models and tool support, making them more applicable in practical settings. The technical advantages of formal verification over manual testing are increasingly recognised in the cybersecurity community. However, for practitioners, formal modelling and verification are often too complex and unfamiliar to be used routinely. In this paper, we present an Eclipse IDE for the design, verification, and implementation of security protocols and evaluate its effectiveness, including feedback from users in educational settings. It offers user-friendly assistance in the formalisation process as part of a Model-Driven Development approach. This IDE centres around the Alice & Bob (AnB) notation, the AnBx Compiler and Code Generator, the OFMC model checker, and the ProVerif cryptographic protocol verifier. For the evaluation, we identify the six most prominent limiting factors for formal method adoption, based on relevant literature in this field, and we consider the IDE's effectiveness against those criteria. Additionally, we conducted a structured survey to collect feedback from university students who have used the toolkit for their projects. The findings demonstrate that this contribution is valuable as a workflow aid and helps users grasp essential cybersecurity concepts, even for those with limited knowledge of formal methods or cryptography. Crucially, users reported that the IDE has been an important component to complete their projects and that they would use again in the future, given the opportunity.

Related papers

• Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization [82.29178197694819]
  We derive and evaluate development-specific prompt optimization guidelines.<n>We use an iterative, test-driven approach to automatically refine code generation prompts.<n>We conduct an assessment with 50 practitioners, who report their usage of the elicited prompt improvement patterns.
  arXiv  Detail & Related papers  (2026-01-19T15:01:42Z)
• Applying Formal Methods Tools to an Electronic Warfare Codebase (Experience report) [5.130336628792388]
  We discuss our experiences with identifying and applying formal methods tools on an electronic warfare (EW) system with stringent safety requirements.<n>We present suggestions for improving formal methods usability including better documentation of capabilities, decreased manual effort, and improved handling of library code.
  arXiv  Detail & Related papers  (2026-01-16T18:46:19Z)
• Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned [45.44933002008943]
  This white paper presents the T"UV AUSTRIA Trusted AI framework.<n>It is an end-to-end audit catalog and methodology for assessing and certifying machine learning systems.<n>Building on three pillars - Secure Software Development, Functional Requirements, and Ethics & Data Privacy - it translates the high-level obligations of the EU AI Act into specific, testable criteria.
  arXiv  Detail & Related papers  (2025-09-08T17:52:08Z)
• ModelForge: Using GenAI to Improve the Development of Security Protocols [1.9241821314180376]
  We introduce ModelForge, a novel tool that automates the translation of protocol specifications.<n>By leveraging advances in Natural Language Processing (NLP) and Generative AI (GenAI), ModelForge processes protocol specifications and generates a CPSA protocol definition.
  arXiv  Detail & Related papers  (2025-06-08T06:27:09Z)
• Towards Mixed-Criticality Software Architectures for Centralized HPC Platforms in Software-Defined Vehicles: A Systematic Literature Review [1.94470674081983]
  We set up a systematic review protocol grounded in established guidelines.<n>Third, we extract key functional domains, constraints, and enabling technologies that drive changes in automotive SWAs.<n>We propose an exemplary SWA for a microprocessor-based system-on-chip.
  arXiv  Detail & Related papers  (2025-06-06T07:40:30Z)
• Advancing Embodied Agent Security: From Safety Benchmarks to Input Moderation [52.83870601473094]
  Embodied agents exhibit immense potential across a multitude of domains. Existing research predominantly concentrates on the security of general large language models. This paper introduces a novel input moderation framework, meticulously designed to safeguard embodied agents.
  arXiv  Detail & Related papers  (2025-04-22T08:34:35Z)
• Using Formal Models, Safety Shields and Certified Control to Validate AI-Based Train Systems [0.5249805590164903]
  The KI-LOK project explores new methods for certifying and safely integrating AI components into autonomous trains. We pursue a two-layered approach: (1) ensuring the safety of the steering system by formal analysis using the B method, and (2) improving the reliability of the perception system with a runtime certificate checker. This work links both strategies within a demonstrator that runs simulations on the formal model, controlled by the real AI output and the real certificate checker.
  arXiv  Detail & Related papers  (2024-11-21T18:09:04Z)
• WIP: An Engaging Undergraduate Intro to Model Checking in Software Engineering Using TLA+ [0.358439716487063]
  We aim to qualitatively assess the state of formal methods in computer science programs. We construct level-appropriate examples that could be included midway into one's undergraduate studies.
  arXiv  Detail & Related papers  (2024-07-30T19:31:14Z)
• Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application [54.984348122105516]
  Large Language Models (LLMs) pretrained on massive text corpus presents a promising avenue for enhancing recommender systems. We propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge.
  arXiv  Detail & Related papers  (2024-05-07T04:00:30Z)
• Testing learning-enabled cyber-physical systems with Large-Language Models: A Formal Approach [32.15663640443728]
  The integration of machine learning (ML) into cyber-physical systems (CPS) offers significant benefits. Existing verification and validation techniques are often inadequate for these new paradigms. We propose a roadmap to transition from foundational probabilistic testing to a more rigorous approach capable of delivering formal assurance.
  arXiv  Detail & Related papers  (2023-11-13T14:56:14Z)
• Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation [79.22678026708134]
  In this paper, we propose an inherently interpretable method, named Transferable Prototype Learning ( TCPL) To achieve this goal, we design a hierarchically prototypical module that transfers categorical basic concepts from the source domain to the target domain and learns domain-shared prototypes for explaining the underlying reasoning process. Comprehensive experiments show that the proposed method can not only provide effective and intuitive explanations but also outperform previous state-of-the-arts.
  arXiv  Detail & Related papers  (2023-10-12T06:36:41Z)
• Use case cards: a use case reporting framework inspired by the European AI Act [0.0]
  We propose a new framework for the documentation of use cases, that we call "use case cards" Unlike other documentation methodologies, we focus on the purpose and operational use of an AI system. The proposed framework is the result of a co-design process involving a relevant team of EU policy experts and scientists.
  arXiv  Detail & Related papers  (2023-06-23T15:47:19Z)
• Lessons from Formally Verified Deployed Software Systems (Extended version) [65.69802414600832]
  This article examines a range of projects, in various application areas, that have produced formally verified systems and deployed them for actual use. It considers the technologies used, the form of verification applied, the results obtained, and the lessons that the software industry should draw regarding its ability to benefit from formal verification techniques and tools.
  arXiv  Detail & Related papers  (2023-01-05T18:18:46Z)
• Constrained Reinforcement Learning for Robotics via Scenario-Based Programming [64.07167316957533]
  It is crucial to optimize the performance of DRL-based agents while providing guarantees about their behavior. This paper presents a novel technique for incorporating domain-expert knowledge into a constrained DRL training loop. Our experiments demonstrate that using our approach to leverage expert knowledge dramatically improves the safety and the performance of the agent.
  arXiv  Detail & Related papers  (2022-06-20T07:19:38Z)
• Evaluating the Safety of Deep Reinforcement Learning Models using Semi-Formal Verification [81.32981236437395]
  We present a semi-formal verification approach for decision-making tasks based on interval analysis. Our method obtains comparable results over standard benchmarks with respect to formal verifiers. Our approach allows to efficiently evaluate safety properties for decision-making models in practical applications.
  arXiv  Detail & Related papers  (2020-10-19T11:18:06Z)
• A Methodology for Creating AI FactSheets [67.65802440158753]
  This paper describes a methodology for creating the form of AI documentation we call FactSheets. Within each step of the methodology, we describe the issues to consider and the questions to explore. This methodology will accelerate the broader adoption of transparent AI documentation.
  arXiv  Detail & Related papers  (2020-06-24T15:08:59Z)

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.