Liang / Kung / Qiu Security and Privacy in Communication Networks

21st EAI International Conference, SecureComm 2025, Xiangtan, China, July 4–6, 2025, Proceedings, Part I
Erscheinungsjahr 2026
ISBN: 978-3-032-23447-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 630 Seiten

Reihe: Computer Science (R0)

This four-volume set LNISCT 687-690 constitutes the proceedings of the 21st EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2025, held in Xiangtan, China, during July 4 - 6, 2025.

The 119 full papers included in these volumes were carefully reviewed and selected from 341 submissions. They are organized in the following topical sections:

Part I: Distributed and Network Security; ML/AI Security.

Part II: ML/AI Security; CyberSecurity.

Part III: CyberSecurity; Cryptography and Authentication.

Part IV: Cryptography and Authentication; Security and Optimization.

Zielgruppe

Research

Autoren/Hrsg.

Liang, Wei

Kung, Sun-Yuan

Qiu, Meikang

Inhaltsverzeichnis

.- Distributed and Network Security.

.- A Fault-Tolerant Block Allocation Scheme for Collaborative Storage Blockchain Systems.

.- A Double-Layer Blockchain-Assisted Anonymous Cross-Domain Authentication and Transaction Scheme for Metaverse.

.- Adaptive On-Chain and Off-Chain Communication Management Based on SDN and Blockchain.

.- BDSV: A Blockchain-based Reliable and Efficient Batch Digital Signature Verification Scheme for IoV.

.- BFT-MS: Asynchronous BFT Protocol Using Bounded Memory.

.- Fair Exchange of Trained Machine Learning Models Based on Permissioned Blockchain and Zero-Knowledge Contingent Payment.

.- SaFeBridge: Consensus-Agnostic Asset Transfer with Slow-Approval Fast-Exit Principles.

.- Security Detection Method for Wireless Sensor Networks Based on Self-Supervised Learning and Deep Learning.

.- Securing Snapshot Pruning in IOTA Tangle 2.0: A Cooperative Deep Reinforcement Learning Approach for Edge-Cloud Smart Meter Networks.

.- TACA: Blockchain-Based Traceable and Anonymous Cross-Domain Authentication Scheme for IIoT.

.- Spatiotemporal Cross-Domain Integrated Insights: Mitigating Fraudulent Activities on Ethereum.

.- PIRchain: Blockchain-Enhanced Privacy-Preserving Inter-domain Routing.

.- Consortium Blockchain-Based Anti-Plagiarism Scheme for Multi-NFT Marketplaces.

.- Cryptocurrency Transaction Anomaly Detection Based on Semi-Supervised Learning and Graph Neural Network.

.- Hardware-assisted Secure Decentralized Cloud Storage via Self-audit and Self-repair.

.- RdBFT: Faster Asynchronous BFT Protocol through Random Binary Agreement.

.- Antitoxin: A Framework for Controlling Persistent Backdoors in Federated Learning.

.- MDC-Net:Multi-Dimensional Cross-Domain Collaborative Network for Image Manipulation Localization.

.- The Framework Of Variational Mode Decomposition Based For DDoS Detection.

.- DCAPSCR: A Decentralized Conditional Anonymous Payment System With Collaborative Regulation.

.- SCFA: Stacked Classifier with Feature Augmentation for Imbalanced Node Classification in Intelligent Internet-of Things.

.- ML/AISecurity.

.- FL(DP)2 : Federated Learning with Dynamic Personalized Differential Privacy.

.- FLARE: Feature-based Lightweight Aggregation for Robust Evaluation of IoT Intrusion Detection.

.- Knowledge Distillation for Federated Learning with Many Noisy Clients.

.- Low Energy Consumption Hierarchical Federated Learning.

.- LPFL-RL: A Lightweight Privacy-Preserving Federated Learning Scheme with Robustness Against Low-Quality Users in Cloud-Edge Collaborative Environments.

.- Heterogeneity-aware semi-asynchronous federated learning.

.- pFedDDS: Personalized Federated Learning via Dual defense Strategies.

.- PPCM-Fed: Privacy-Preserving Cross-Modal Federated Learning in IoT.

.- Security-Aware and Energy-Efficient Federated Learning in LEO Satellite Edge Micro-clouds: A Noise-Adaptive Allocation Framework.