Model-Based Safety and Assessment

9th International Symposium, IMBSA 2025, Athens, Greece, September 24–26, 2025, Proceedings

Häftad, Engelska, 2025

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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.

Produktinformation

Utgivningsdatum2025-09-21
Mått155 x 235 x 25 mm
Vikt698 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor446
FörlagSpringer Nature Switzerland AG
ISBN9783032050724

Tillhör följande kategorier

Hårdvara inom Data och it
Systemvetenskap och AI inom Data och it

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.