Katsikas / Xenakis / Kalloniatis Information and Communications Security

26th International Conference, ICICS 2024, Mytilene, Greece, August 26–28, 2024, Proceedings, Part I
Erscheinungsjahr 2024
ISBN: 978-981-97-8798-2
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 15056, 332 Seiten, eBook

Reihe: Lecture Notes in Computer Science

This two-volume proceedings set LNCS 15056-15057 constitutes the proceedings of 26th International Conference on Information and Communications Security, ICICS 2024, in Mytilene, Greece, during August 26-28, 2024.The 32 full papers presented in this book were carefully selected and reviewed from 123 submissions. They cover topics related to many aspects of security in information and communication systems, ranging from attacks, to defences, to trust issues, to anomaly-based intrusion detection, to privacy preservation, and to theory and applications of various cryptographic techniques.

Zielgruppe

Research

Autoren/Hrsg.

Katsikas, Sokratis

Xenakis, Christos

Kalloniatis, Christos

Lambrinoudakis, Costas

Inhaltsverzeichnis

.- Attacks.
.- How to Design Honey Vault Schemes.
.- New Result for Breaking NTRU Encryption with Multiple Keys in Polynomial Time..- Improving Differential-Neural Cryptanalysis for Large-State SPECK..- Evasion attempt for the malicious PowerShell detector considering feature weights..- A Fast Framework for Efficiently Constructing Valuable Cubes..- A Survey on Acoustic Side Channel Attacks on Keyboards..- Trust..- Enhancing TrUStAPIS Methodology in the Web of Things with LLM-generated IoT Trust Semantics..- DMA: Mutual Attestation Framework for Distributed Enclaves..- Cabin: Confining Untrusted Programs within Confidential VM..- Anomaly detection.
.- UARC:Unsupervised Anomalous Traffic Detection with Improved U-shaped Autoencoder and RetNet based Multi-Clustering.
.- An Investigation into the Performance of Non-Contrastive Self-Supervised Learning Methods for Network Intrusion Detection..- Impact of Recurrent Neural Networks and Deep Learning Frameworks on Adaptive Real-time Lightweight Time Series Anomaly Detection..- Privacy..- Secure and Robust Privacy-Preserving Federated Learning For Heterogeneous Resource..- Privacy-preserving Logistic Regression Model Training Scheme by Homomorphic Encryption..- Privacy preserving and verifiable outsourcing of AI processing for cyber-physical systems..- Investigating the Privacy Risk of using Robot Vacuum Cleaners in Smart Environments.