A survey that examines fragmentation in existing 6G security approaches, develops a cross-layer threat taxonomy, maps countermeasures, and identifies research gaps for trustworthy AI-native 6G ecosystems.
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2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A trust-aware federated hybrid intrusion detection framework using random forest, decision tree, and linear SVM is proposed for intelligent transport systems with edge computing.
citing papers explorer
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Toward a Unified Security and Privacy Framework for AI-Native 6G Networks
A survey that examines fragmentation in existing 6G security approaches, develops a cross-layer threat taxonomy, maps countermeasures, and identifies research gaps for trustworthy AI-native 6G ecosystems.
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A Comparative Analysis of Machine Learning Models for Intrusion Detection in Intelligent Transport Systems
A trust-aware federated hybrid intrusion detection framework using random forest, decision tree, and linear SVM is proposed for intelligent transport systems with edge computing.