Nyhet

Security and Privacy in Communication Networks

21st EAI International Conference, SecureComm 2025, Xiangtan, China, July 4–6, 2025, Proceedings, Part I

Häftad, Engelska, 2026

Del 687 i serien Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2026-04-24
Mått:155 x 235 x 35 mm
Vikt:961 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Antal sidor:630
Förlag:Springer Nature Switzerland AG
ISBN:9783032234469

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Referensverk och tvärvetenskap inom Samhälle och politik
IT-säkerhet inom Data och IT
Nätverk och kommunikation inom Data och IT

Innehållsförteckning

.- 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/AI Security..- 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.