Nyhet

Machine Learning for Cyber Security

7th International Conference, ML4CS 2025, Hangzhou, China, December 12–14, 2025, Proceedings

AvYang Xiang,Jian Shen

Häftad, Engelska, 2026

Del i serien Lecture Notes in Computer Science

876 kr

Kommande

Beskrivning

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.

Produktinformation

Utgivningsdatum:2026-05-01
Mått:155 x 235 x undefined mm
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:284
Förlag:Springer Verlag, Singapore
ISBN:9789819578191

Utforska kategorier

Hårdvara inom Data och IT
IT-säkerhet inom Data och IT
Nätverk och kommunikation inom Data och IT

Innehållsförteckning

.- 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.