DoH deception: evading ML-based tunnel detection models with real-world adversarial examples (2024)
Source: Anais. Conference titles: Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais - SBSeg. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ROBUSTEZ, SEGURANÇA DE REDES
ABNT
VALENTE, Emanuel C. A et al. DoH deception: evading ML-based tunnel detection models with real-world adversarial examples. 2024, Anais.. Porto Alegre: SBC, 2024. Disponível em: https://doi.org/10.5753/sbseg.2024.241637. Acesso em: 27 nov. 2025.APA
Valente, E. C. A., Osti, A. A., Pereira Júnior, L. A., & Estrella, J. C. (2024). DoH deception: evading ML-based tunnel detection models with real-world adversarial examples. In Anais. Porto Alegre: SBC. doi:10.5753/sbseg.2024.241637NLM
Valente ECA, Osti AA, Pereira Júnior LA, Estrella JC. DoH deception: evading ML-based tunnel detection models with real-world adversarial examples [Internet]. Anais. 2024 ;[citado 2025 nov. 27 ] Available from: https://doi.org/10.5753/sbseg.2024.241637Vancouver
Valente ECA, Osti AA, Pereira Júnior LA, Estrella JC. DoH deception: evading ML-based tunnel detection models with real-world adversarial examples [Internet]. Anais. 2024 ;[citado 2025 nov. 27 ] Available from: https://doi.org/10.5753/sbseg.2024.241637
