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arXiv Machine Learning By Diego Cajaraville-Aboy, Marta Moure-Garrido, Carlos Beis-Penedo, Carlos Garcia-Rubio, Rebeca P. D\'iaz-Redondo, Celeste Campo, Ana Fern\'andez-Vilas, Manuel Fern\'andez-Veiga

CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection

Read the original on arXiv Machine Learning →

arXiv:2504. 01882v2 Announce Type: replace Abstract: The use of DNS over HTTPS (DoH) tunneling by an attacker to hide malicious activity within encrypted DNS traffic poses a serious threat to network security, as it allows malicious actors to bypass traditional monitoring and intrusion detection systems while evading detection by conventional traffic analysis techniques.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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