Information and Communications Security : 27th International Conference, ICICS 2025, Nanjing, China, October 29-31, 2025, Proceedings, Part III (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 III (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 584 p.
  • 言語 ENG
  • 商品コード 9789819535361

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

Attack and Defense

.- Domain Adaptation for Cross-Device Profiled ML Side-Channel Attacks.

.- Find the Clasp of the Chain: Efficiently Locating Cryptographic Procedures in SoC Secure Boot by Semi-automated Side-Channel Analysis.

.- Full-phase distributed quantum impossible differential cryptanalysis.

.- ProverNG: Efficient Verification of Compositional Masking for Cryptosystem's Side-Channel Security.

.- POWERPOLY: Multilingual Program Analysis with the Aid of WebAssembly.

.- Not only spatial, but also spectral: Unnoticeable backdoor attack on 3D point clouds.

.- Permutation-Based Cryptanalysis of the SCARF Block Cipher and Its Randomness Evaluation.

.- Secure and Scalable TLB Partitioning Against Timing Side-Channel Attacks.

.- Security Vulnerabilities in AI-Generated Code: A Large-Scale Analysis of Public GitHub Repositories.

.- Vulnerability Analysis.

.- Towards Efficient C/C++ Vulnerability Impact Assessment in Package Management Systems.

.- AugGP-VD: A smart contract vulnerability detection approach based on augmented graph convolutional networks and pooling.

.- VULDA: Source Code Vulnerability Detection via Local Dependency Context Aggregation on Vulnerability-aware Code Mapping Graph.

.- KVT-Payload: Knowledge Graph-Enhanced Hierarchical Vulnerability Traffic Payload Generation.

.- Construction and Application of Vulnerability Intelligence Ontology under Vulnerability Management Perspective.

.- Anomaly Detection.

.- Speaker Inference Detection Using Only Text.

.- DTGAN: Diverse-Task Generative Adversarial Networks for Intrusion Detection Systems Against Adversarial Examples.

.- ConComFND: Leveraging Content and Comment Information for Enhanced Fake News Detection.

.-Transferable Adversarial Attacks in Object Detection: Leveraging Ensemble Features and Gradient Variance Minimization.

.- VAE-BiLSTM: A Hybrid Model for DeFi Anomaly Detection Combining VAE and BiLSTM.

.- FluxSketch: A Sketch-based Solution for Long-Term Fluctuating Key Flow Detection.

.- RustGuard: Detecting Rust Data Leak Issues with Context-Sensitive Static Taint Analysis.

.- Secure Guard: A Semantic-Based Jailbreak Prompt Detection Framework for Protecting Large Language Models.

.- Traffic Classification.

.- FCAL: An Asynchronous Federated Contrastive Semi-Supervised Learning Approach for Network Traffic Classification.

.- TetheGAN: A GAN-Based Synthetic Mobile Tethering Traffic Generating Framework.

.- SPTC: Signature-based Cross-protocol Encrypted Proxy Traffic Classification Approach.

.- Multi-modal Datagram Representation with Spatial-Temporal State Space Models and Inter-flow Contrastive Learning for Encrypted Traffic Classification.

.- FlowGraphNet: Efficient Malicious Traffic Detection via Graph Construction.

.- CascadeGen: A Hybrid GAN-Diffusion Framework for Controllable and Protocol-Compliant Synthetic Network Traffic Generation.

.- Steganography and Watermarking.

.- Towards High-Capacity Provably Secure Steganography via Cascade Sampling.

.- When There Is No Decoder: Removing Watermarks from Stable Diffusion Models in a No-box Setting.

.- Robust Reversible Watermarking for 3D Models Based on Auto Diffusion Function.

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