Xiang / Shen Machine Learning for Cyber Security

7th International Conference, ML4CS 2025, Hangzhou, China, December 12–14, 2025, Proceedings
Erscheinungsjahr 2026
ISBN: 978-981-957820-7
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 284 Seiten

Reihe: Springer Nature Proceedings Computer Science

This book constitutes the proceedings of the 7th International Conference on Machine Learning for Cyber Security, ML4CS 2025, which taking place during December 12-14, 2025 held in Hangzhou, China.

The 18 full papers presented in this book were carefully reviewed and selected from 97 submissions. ML4CS is a well-recognized annual international forum for AI-driven security researchers to exchange ideas and present their works. The conference focus on topics such as blockchain, network security, system security, software security, threat intelligence, cybersecurity situational awareness and much many more.  

Zielgruppe

Research

Autoren/Hrsg.

Xiang, Yang

Shen, Jian

Inhaltsverzeichnis

.- Blockchain-based Cross-Domain Data Auditing Scheme for E-Commerce AI.
.- DFI-GNN: A Dual-Feature Interaction Graph Neural Network Model for Multimodal Medical Image Classification.
.- A Fast-Verifiable Threshold BLS Signature Scheme.
.- Cracking Passwords with Large Language Models by Exploiting Linguistic Features.
.- AgentGuard: An Active Threat Discovery System for Package Confusion using Multi-Agent Collaboration.
.- Parallelizable Oblivious Non-Equi-Joins in Trusted Execution Environments.
.- Blockchain-Based Anonymous Aggregate Signature Scheme for Medical Internet of Things.
.- Evidential Deep Fusion for Multi-Channel Analysis against Public-Key Cryptosystems.
.- Token-Efficient Binary Vulnerability Prioritization via Function Pre-Filtering with LLMs.
.- A Prefix-Based Homomorphic Encryption Protocol for Efficient Secure Comparison.
.- A Verifiable Data Possession Scheme for Distributed Computing.
.- Entropy-Aware Watermarking for Code Generation Models.
.- A Verifiable and Privacy-Preserving Federated Learning Framework via Homomorphic Encryption.
.- Empirical Study on Adversarial Robustness Degradation in Image Classification via Unlearning.
.- P2FR-VFL: Privacy-Enhanced Vertical Federated Learning Framework via P2FR-PSI and Homomorphic 
Encryption.
.- Performance Evaluation of Parallel Inference Pipeline for Multi-Model Processing on Edge Devices.
.- Multi-Authority Attribute-Based Access Control with Dynamic Policy Updates for Federated Learning.