Availability, Reliability and Security 20th International Conference, ARES 2025, Ghent, Belgium, August 11–14, 2025, Proceedings, Part II

This two-volume set LNCS 15992-15993 constitutes the proceedings of the 20th International Conference on Availability, Reliability and Security, ARES 2025, in Ghent, Belgium, during August 11-14, 2025.
The 34 full papers presented in this book together with 8 short papers were carefully reviewed and selected from 186 submissions.
They cover topics such as: Privacy-Enhancing Technologies and Legal Compliance; Network and Communication Security; IoT and Embedded Systems Security; Machine Learning and Privacy; Usable Security and Awareness; System Security; Supply Chain Security, Malware and Forensics; and Machine Learning and Security.

Usable Security and Awareness: QRisk: Think Before You Scan QR codes.- Evaluating Argon2 Adoption and Effectiveness in Real-World Software.- AdvisoryHub: Design and Evaluation of a Cross-Platform Security Advisory System for Cyber Situational Awareness.- Service-aware password risk meter – Helping users to choose suitable passwords in services. System Security: TEE-Assisted Recovery and Upgrades for Long-Running BFT Services.- Fast and Efficient Secure L1 Caches for SMT.- FatPTE - Expanding Page Table Entries for Security.- CHERI UNCHAINED: Generic Instruction and Register Control for CHERI Capabilities.- Exploring speculation barriers for RISC-V selective speculation.- Do we still need canaries in the coal mine? Measuring shadow stack effectiveness in countering stack smashing. Supply Chain Security, Malware and Forensics: SoK: Towards Reproducibility for Software Packages in Scripting Language Ecosystems.- Clustering Malware at Scale: A First Full-Benchmark Study.- Advances in Automotive Digital Forensics: Recent Trends and Future Directions.- Exploring the Susceptibility to Fraud of Monetary Incentive Mechanisms for Strengthening FOSS Projects. Machine Learning and Security: Multi-Agent Simulation and Reinforcement Learning to Optimize Moving Target Defense.- LeaX: Class-Focused Explanations for Locating Leakage in Learning-based Profiling Attacks.- Large Language Models are Unreliable for Cyber Threat Intelligence.- Augmented Tabular Adversarial Evasion Attacks with Constraint Satisfaction Guarantees.- TTP Classification with Minimal Labeled Data: A Retrieval-Based Few-Shot Learning Approach.- C2 Beaconing Detection via AI-based Time-Series Analysis.- Fooling Rate and Perceptual Similarity: A Study on the Effectiveness and Quality of DCGAN-based Adversarial Attacks.