Model-Based Safety and Assessment (eBook, PDF)

9th International Symposium, IMBSA 2025, Athens, Greece, September 24-26, 2025, Proceedings
Redaktion: Katsaros, Panagiotis

• Format: PDF

Produktbeschreibung

This book LNCS 15755 constitutes the proceedings of the 9th International Symposium on Model-Based Safety and Assessment, IMBSA 2025, held in Athens, Greece, in September 24-26, 2025.

The 28 full papers were carefully reviewed and selected from 39 submissions. The proceedings focus on System Safety Assessment, Cybersecurity Analysis, Safe Machine Learning, Probabilistic Analysis, Model-based Design and Safety Assessment, Machine Learning and Automata Learning for System Safety, Failure Detection Isolation and Recovery Analysis.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 446
• Erscheinungstermin: 20. September 2025
• Englisch
• ISBN-13: 9783032050731
• Artikelnr.: 75488784

Inhaltsangabe

System Safety Assessment.- Failure and defect detection of safety critical 3D printed goods.- Model-Based Safety Assessment for Flight Control Systems: Methodology and Case Study.- Multi-approach based Safety Analysis of a Wastewater Treatment System.- Application of a MBSA approach on a representative subsystem of EGNOS (European Geostationary Navigation Overlay Service).- Safety Analysis Methods in Aerospace: A Case-Based Comparison of FTA and MBSA.- Cybersecurity Analysis.- MBCA: A Model-Based Approach for Cybersecurity Analysis of Cyber-Physical Systems.- Cybersecurity Threat Detection through Business Process Log Analysis.- Interpretable and Trustworthy Attack Diagnosis for UAVs Using SafeML.- Safe Machine Learning.- Incorporating failure of Machine Learning in probabilistic safety assessment and runtime safety assurance.- Safer Skin Lesion Classification with Global Class Activation Probability Map Evaluation and SafeML.- CODIF: Counterfactual data-augmentations for estimating perception influencing factors.- The Information Meta Model for Machine Learning IM3L: A Structured Approach to ML Integration in Engineering Systems.- RAGuard: A Novel Approach for in-context Safe Retrieval Augmented Generation for LLMs.- Probabilistic Analysis.- Variance-based Sensitivity Analysis for Probabilistic Risk Assessment.- Causal Bayesian Networks for Data-driven Safety Analysis of Complex Systems.- Model-based Design and Safety Assessment.- From Natural Language Requirement Specifications to Logic Properties.- Model-Based Dependent Failure Analysis.- Comparative Analysis of Non-Colored and Colored Petri Net Models for Availability Assessment of Safety-Critical Cloud Software in Railways.- MBSA model exchange and its challenges.- ACEditor: a Modeling Tool for Synthesizing Exceutable Assurance Cases from Fault Trees.- Machine Learning and Automata Learning for System Safety.- AI4Green, A Framework for AI-based Resource Optimizations for Reliable Applications.- Analyzing Truck Platoons with Automata Learning and Model Checking.- Q-SafeML, A Quantum-Statistical Approach to Safety Monitoring in Quantum Machine Learning.- Failure Detection Isolation and Recovery Analysis.- Towards a Unifying View of Fault Propagation Analyses and Notations.- An Altarica-based modelling and analysis approach enabling UAV regulation compliance.- Timed Models in AltaRica 3.0.- Experience in developing an algorithm at the MBSA level to minimize the complexity of fault trees during automatic generation from design data.- From Abstract to Action: Tailored Environment Taxonomies for More Complete ADS Safety Analyses.