Computer Safety, Reliability, and Security

SAFECOMP 2019 Workshops, ASSURE, DECSoS, SASSUR, STRIVE, and WAISE, Turku, Finland, September 10, 2019, Proceedings
Herausgegeben:Romanovsky, Alexander; Troubitsyna, Elena; Gashi, Ilir; Schoitsch, Erwin; Bitsch, Friedemann

• Broschiertes Buch

Produktbeschreibung

This book constitutes the proceedings of the Workshops held in conjunction with SAFECOMP 2019, 38th International Conference on Computer Safety, Reliability and Security, in September 2019 in Turku, Finland.

The 32 regular papers included in this volume were carefully reviewed and selected from 43 submissions; the book also contains two invited papers. The workshops included in this volume are:

ASSURE 2019:
7th International Workshop on Assurance Cases for Software-Intensive Systems

DECSoS 2019:
14th ERCIM/EWICS/ARTEMIS Workshop on Dependable Smart Embedded and Cyber-Physical Systems and Systems-of-Systems

SASSUR 2019:
8th International Workshop on Next Generation of System Assurance Approaches for Safety-Critical Systems

STRIVE 2019:
Second International Workshop on Safety, securiTy, and pRivacy In automotiVe systEms

WAISE 2019:
Second International Workshop on Artificial Intelligence Safety Engineering

Produktdetails

• Lecture Notes in Computer Science 11699
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-26249-5
• 1st ed. 2019
• Seitenzahl: 432
• Erscheinungstermin: 9. August 2019
• Englisch
• Abmessung: 235mm x 155mm x 24mm
• Gewicht: 644g
• ISBN-13: 9783030262495
• ISBN-10: 3030262499
• Artikelnr.: 56999139

Inhaltsangabe

7th International Workshop on Assurance Cases for Software-intensive Systems (ASSURE 2019).- Combining GSN and STPA for safety arguments.- A modelling approach for system life cycles assurance.- Contract-based Modular Safety Cases for Incremental Certification of Product Lines.- 14th International ERCIM/EWICS/ARTEMIS Workshop on Dependable Smart Cyber-Physical Systems and Systems-of-Systems (DECSoS 2019).- Comparative Evaluation of Security Fuzzing Approaches.- Assuring compliance with protection profiles with Threatget.- A Survey on the Applicability of Safety, Security and Privacy Standards in Developing Dependable Systems.- Combined Approach for Safety and Security.- Towards Integrated Quantitative Security and Safety Risk Assessment.- Potential Use of Safety Analysis for Risk Assessments in Smart City Sensor Network Applications.- Increasing Safety of Neural Networks in Medical Devices.- Smart Wristband for Voting.- 8th International Workshop on Next Generation of System Assurance Approaches for Safety-Critical Systems (SASSUR 2019).- Automotive Cybersecurity standards - relation and overview.- A Runtime Safety Monitoring Approach for Adaptable Autonomous Systems.- Structured Reasoning for Socio-Technical Factors of Safety-Security Assurance.- The SISTER approach for Verification and Validation: a lightweight process for reusable results.- Introduction to the Safecomp 2018 Workshop on Safety, securiTy, and pRivacy In automotiVe systEms (STRIVE 2018).- Demo: CANDY CREAM.- CarINA - Car sharing with IdeNtity based Access control re-enforced by TPM.- Combining Safety and Security in Autonomous Cars Using Blockchain Technologies.- Enhancing CAN security by means of lightweight stream-ciphers and protocols.- Analysis of Security Overhead in Broadcast V2V Communications.- You overtrust your printer.- 2nd International Workshop on Artificial Intelligence Safety Engineering (WAISE 2018).- Three Reasons Why: Framing the Challenges of Assuring AI.- Improving ML Safety with Partial Specifications.- An Abstraction-Refinement Approach to Formal Verification of Tree Ensembles.- RL-Based Method for Benchmarking the Adversarial Resilience and Robustness of Deep Reinforcement Learning Policies.- A Safety Standard Approach for Fully Autonomous Vehicles (Position Paper).- Open Questions in Testing of Learned Computer Vision Functions for Automated Driving.- Adaptive Deployment of Safety Monitors for Autonomous Systems.- Uncertainty Wrappers for Data-driven Models - Increase the Transparency of AI/ML-based Models through Enrichment with Dependable Situation-aware Uncertainty Estimates.- Confidence Arguments for Evidence of Performance in Machine Learning for Highly Automated Driving Functions.- Bayesian Uncertainty Quantification with Synthetic Data.- A Self-Certifiable Architecture for Critical Systems Powered by Probabilistic Logic Artificial Intelligence.- Tackling Uncertainty in Safety Assurance for Machine Learning: Continuous Argument Engineering with Attributed Tests.- The Moral Machine: Is It Moral.