Machine Learning for Cyber Security (eBook, PDF)

6th International Conference, ML4CS 2024, Hangzhou, China, December 27-29, 2024, Proceedings
Redaktion: Xiang, Yang; Shen, Jian

• Format: PDF

Produktbeschreibung

This book constitutes the referred proceedings of the 6th International Conference on Machine Learning for Cyber Security, ML4CS 2024, held in Hangzhou, China, during December 27-29, 2024.

The 30 full papers presented in this book were carefully reviewed and selected from 111 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.

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Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 450
• Erscheinungstermin: 31. März 2025
• Englisch
• ISBN-13: 9789819645664
• Artikelnr.: 73759354

Inhaltsangabe

.- Secure Resource Allocation via Constrained Deep Reinforcement Learning.

.- Efficient Two-Party Privacy-Preserving Ridge and Lasso Regression via SMPC.

.- A Decentralized Bitcoin Mixing Scheme Based on Multi-signature.

.- Decentralized Continuous Group Key Agreement for UAV Ad-hoc Network.

.- Efficient Homomorphic Approximation of Max Pooling for Privacy-Preserving Deep Learning.

.- Blockchain-Aided Revocable Threshold Group Signature Scheme for Smart Grid.

.- Privacy-preserving Three-factors Authentication and Key Agreement for Federated Learnin.

.- Blockchain-Based Anonymous Authentication Scheme with Traceable Pseudonym Management in ITS.

.- Multi-keyword Searchable Data Auditing for Cloud-based Machine Learning.

.- A Flexible Keyword-Based PIR Scheme with Customizable Data Scales for Multi-Server Learning.

.- Automatic Software Vulnerability Detection in Binary Code.

.- Malicious Code Detection Based On Generative Adversarial Model.

.- Construction of an AI Code Defect Detection and Repair Dataset Based on Chain of Thought.

.- Backdoor Attack on Android Malware Classifiers Based on Genetic Algorithms.

.- A Malicious Websites Classifier Based on an Improved Relation Network.

.- Unknown Category Malicious Traffic Detection Based on Contrastive Learning.

.- SoftPromptAttack: Research on Backdoor Attacks in Language Models Based on Prompt Learning.

.- Removing Regional Steering Vectors to Achieve Knowledge Domain Forgetting in Large Language Models.

.- A Novel and Efficient Multi-scale Spatio-temporal Residual Network for Multi-Class Instrusion Detection.

.- Provable Data Auditing Scheme from Trusted Execution Environment.

.- Enhanced PIR Scheme Combining SimplePIR and Spiral: Achieving Higher Throughput without Client Hints.

.- A Two-stage Image Blind Inpainting Algorithm Based on Gated Residual Connection.

.- GAN-based Adaptive Trigger Generation and Target Gradient Alignment in Vertical Federated Learning Backdoor Attacks.

.- Weakly Supervised Waste Classification with Adaptive Loss and Enhanced Class Activation Maps.

.- A Vehicle Asynchronous Communication Scheme Based on Federated Deep Reinforcement Learning.

.- A Vehicles Scheduling Algorithm Based on Clustering based Federated Learning.

.- A Cooperative Caching Strategy Based on Deep Q-Network for Mobile Edge Networks.

.- YOLO-LiteMax: An Improved Model for UAV Small Object Detection.

.- LMCF-FS: A Novel Lightweight Malware Classification Framework Driven by Feature Selection.

.- Rule Learning-Based Target Prediction for Efficient and Flexible Private Information Retrieval.