Machine learning for cyber security ...
ML4CS (Conference) (2024 :)

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  • Machine learning for cyber security = 6th International Conference, ML4CS 2024, Hangzhou, China, December 27-29, 2024 : proceedings /
  • Record Type: Electronic resources : Monograph/item
    Title/Author: Machine learning for cyber security/ edited by Yang Xiang, Jian Shen.
    Reminder of title: 6th International Conference, ML4CS 2024, Hangzhou, China, December 27-29, 2024 : proceedings /
    remainder title: ML4CS 2024
    other author: Xiang, Yang.
    corporate name: ML4CS (Conference)
    Published: Singapore :Springer Nature Singapore : : 2025.,
    Description: xiii, 450 p. :ill., digital ;24 cm.
    [NT 15003449]: 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.
    Contained By: Springer Nature eBook
    Subject: Machine learning - Congresses. -
    Online resource: https://doi.org/10.1007/978-981-96-4566-4
    ISBN: 9789819645664
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