Information and Communications Security : 27th International Conference, ICICS 2025, Nanjing, China, October 29-31, 2025, Proceedings, Part II (Lecture Notes in Computer Science)

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Information and Communications Security : 27th International Conference, ICICS 2025, Nanjing, China, October 29-31, 2025, Proceedings, Part II (Lecture Notes in Computer Science)

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 553 p.
  • 言語 ENG
  • 商品コード 9789819535422

Full Description

This three-set volume LNCS 16217-16219 constitutes the refereed proceedings of 27th International Conference on Information and Communications Security, ICICS 2025, held in Nanjing, China, during October 29-31, 2025.

The 91 full papers presented in this book were carefully selected and reviewed from 357 submissions. The papers are organized in the following topical sections:

Part I: Cryptography; Post-quantum Cryptography; Anonymity and Privacy; Authentication and Authorization.

Part II: Blockchain and Cryptocurrencies, System and Network Security, Security and Privacy of AI, Machine Learning for Security. 

Part III: Attack and Defense; Vulnerability Analysis; Anomaly Detection; Traffic Classification; Steganography and Watermarking.

Contents

.- Blockchain and Cryptocurrencies.

.- EquinoxBFT: BFT Consensus for Blockchain Emergency Governance.

.- fFuzz: A State-aware Function-level Fuzzing Framework for Smart Contract Vulnerabilities Detection.

.- TraceBFT: Backtracking-based Pipelined Asynchronous BFT Consensus for High-Throughput Distributed Systems.

.- RADIAL: Robust Adversarial Discrepancy-aware Framework for Early Detection of Illicit Cryptocurrency Accounts.

.- Enhancing Private Signing Key Protection in Digital Currency Transactions Using Obfuscation.

.- AnsBridge: Towards Secure Cross-Chain Interoperability via Anonymous and Verifiable Validators.

.- TrustBlink: A zkSNARK-Powered On-Demand Relay for PoW Cross-Chain Verification With Low Cost.

.- R1-MFSol: a Smart Contract Vulnerability Detection Model Based on LLM and Multi-modal Feature Fusion.

.- No Place to Hide: An Efficient and Accurate Backdoor Detection Tool for Ethereum ERC-20 Smart Contracts.

.- System and Network Security.

.- Batch-oriented Element-wise Approximate Activation for Privacy-Preserving Neural Networks.

.- Social-Aware and Quality-Driven Incentives for Mobile Crowd-Sensing with Two-Stage Game.

.- A Distributed Privacy Protection Method for Crowd Sensing Based on Trust Evaluation.

.- DBG-LB: A Trustworthy and Efficient Framework for Data Sharing in the Internet of Vehicles.

.- Actions Speak Louder Than Words: Evidence-Based Trust Level Evaluation in Multi-Agent Systems.

.- Bridging the Interoperability Gaps Among Trusted Architectures in MCUs.

.- Security and Privacy of AI.

.- A Dropout-Resilient and Privacy-Preserving Framework for Federated Learning via Lightweight Masking.

.- AFedGAN: Adaptive Federated Learning with Generative Adversarial Networks for Non-IID Data.

.- OTTER: Optimized Training with Trustworthy Enhanced Replication via Diffusion and Federated VMUNet for Privacy-Aware Medical Segmentatio.

.- EAGLE: Ensemble Adaptive Graph Learning for Enhanced Ethereum Fraud Detection.

.- BR-CPPFL: A Blockchain-based Robust Clustered Privacy-preserving Federated Learning System.

.- Efficient Semi-asynchronous Federated Learning with Guided Selective Participation and Adaptive Aggregation.

.- Improving Byzantine-resilience in Federated Learning via Diverse Aggregation and Adaptive Variance Reduction.

.- Hierarchical Recovery of Convolutional Neural Networks via Self-Embedding Watermarking.

.- Personalized Federated Learning Algorithm Based on User Grouping and Group Signatures.

.- Machine Learning for Security.

.- SPCD: A Shot-Based Partial Copy Detection Method.

.- Bayesian-Adaptive Graph Neural Network for Anomaly Detection (BAGNN).

.- UzPhishNet Model for Phishing Detection.

.- CyberNER-LLM: Cyber Threat Intelligence Named Entity Recognition With Large Language Model.

.- Provenance-Based Intrusion Detection via Multi-Scale Graph Representation Learning.

.- SADGA: A Self Attention GAN-Based Adversarial DGA with High Anti-Detection Ability.

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