Benchmarking the adversarial resilience of machine learning models for DDoS detection

Distributed Denial of Service (DDoS) attacks continue to grow in scale and sophistication, making timely and reliable detection increasingly challenging. Machine learning (ML) models have demonstrated promise in identifying malicious traffic patterns. However, their vulnerability to adversarial mani...

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Bibliografiske detaljer

Principais autores:	Harsh Dadhwal, Mateus de Abreu, Nazanin Parvizi, Sajal Saha
Format:	Artigo
Sprog:	Inglês
Udgivet:	Elsevier 2026-03-01
Serier:	Array
Fag:	Adversarial machine learning Adversarial training DDoS attack detection FGSM Model robustness Network intrusion detection
Online adgang:	http://www.sciencedirect.com/science/article/pii/S2590005625002917
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