Overview

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.

Full Product Details

Author:   Wei Liang ,  Sun-Yuan Kung ,  Meikang Qiu
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
ISBN:  

9783032234490

ISBN 10:   3032234492
Publication Date:   23 May 2026
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Forthcoming
Availability:   Not yet available  
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Table of Contents

.- ML/AI Security. .- SVD-Based Efficient Communication Scheme for Heterogeneous Federated Learning. .- IncreTTP: An Incremental TTPs Classification Model. .- Login, Logout, Reset: Measuring Security and Privacy Issues in Real-World Web Logins. .- Location Privacy Protection for VANETs based on Dynamically Adjustable Security Coefficient. .- BPGT: A Novel Privacy-Preserving K-Means Clustering Framework to Guarantee Local $d_{\chi}$-privacy. .- Hamk-CFI: A Hardware-assisted Kernel Control Flow Protection Framework For Cloud Environments. .- PEARL: A Reinforcement Learning-Based Attack Analysis Approach for Security and Robustness Assessment of Smart Home Systems. .- The RPKI and Its Hidden Guardians: An Empirical Analysis of Their Relationship and Security Implications. .- FedCC: Robust Federated Learning against Model Poisoning Attacks. .- FedCLF: Contrastive Learning-driven Framework for Mitigating Data Heterogeneity in Federated Learning. .- FedSND:Federated Learning with Symmetric Noise Decentralized Orthogonal Encryption. .- Group-Based Parallel Split Federated Learning. .- A Verifiable Federated Learning Aggregation Scheme Based on Homomorphic Hashing. .- A Lightweight Image Steganography Scheme Based on Invertible Neural Network Architecture with Progressive Channel Attention. .- Defending Against Malicious Clients in Robust Heterogeneous Federated Learning. .- CyberSecurity. .- A Few-Shot-Based Model-Agnostic Meta-Learning for Intrusion Detection in Secure of In-vehicle Network. .- A Robustness Optimization Mechanism for Intrusion Detection Models Based on Dynamic Ensemble Learning. .- BFDet: A Method for Detecting Malicious Traffic with Ultra-Low False Positive Rate Based on TLS Behavior Flow. .- DARD: Dice Adversarial Robustness Distillation Against Adversarial Attacks. .- EvasionEval: A Benchmark for LLMs in Evaluating Advanced Defense Evasion Techniques. .- Flow Dissector: A Flow Slicing Representation with a Pre-trained Model for Varied Malicious Traffic Classification. .- From Dark Network to Honeypot: Analysis and Traceability of Multi-prefix IPv6 Network Attacks. .- Insights into Ransomware Detection based on Semantic Understanding. .- Log Anomaly Detection based on Time-Delta Sequential Feature. .- Log Semantic Parsing based on Sequence Annotation in Security Operations. .- MOT-Fuzz: A Novel Directed Greybox Fuzzing with Multiple Ordered Target Basic Blocks for Multistep Vulnerabilities. .- MFRWF: Enhance Website Fingerprinting Robustness Against website content updates and background noise traffic. .- One Trace is Possible: A Method for Small-Sample Profiling Side-Channel Analysis in Communication Environments. .- Task-Driven GAN for Class-Imbalanced Intrusion Detection. .- Two Birds with One Stone: Multi-Task Detection and Attribution of LLM-Generated Text.

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