MetaCIDS: Privacy-Preserving Collaborative Intrusion Detection for Metaverse based on Blockchain and Online Federated Learning
Author:
Affiliation:
1. Institut National de la Recherche Scientifique, University of Québec, Montréal, QC, Canada
Funder
Innovation for Defence Excellence and Security
Ministère de la Défense Nationale
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Computer Science
Link
http://xplorestaging.ieee.org/ielx7/8782664/10016900/10239541.pdf?arnumber=10239541
Reference35 articles.
1. A Deep Blockchain Framework-Enabled Collaborative Intrusion Detection for Protecting IoT and Cloud Networks
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4. A detailed analysis of the KDD CUP 99 data set
5. Towards Blockchain-Based Collaborative Intrusion Detection Systems
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2. Securing the Metaverse: A Deep Reinforcement Learning and Generative Adversarial Network Approach to Intrusion Detection;2024 IEEE International Conference on Communications Workshops (ICC Workshops);2024-06-09
3. Blockchain-Inspired Collaborative Cyber-Attacks Detection for Securing Metaverse;IEEE Internet of Things Journal;2024-05-15
4. Detecting cyberthreats in Metaverse learning platforms using an explainable DNN;Internet of Things;2024-04
5. Bit-by-Bit: A Quantization-Aware Training Framework with XAI for Robust Metaverse Cybersecurity;2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC);2024-02-19
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