Unveiling DoH tunnel: Toward generating a balanced DoH encrypted traffic dataset and profiling malicious behavior using inherently interpretable machine learning.

MLA

Niktabe, Sepideh, et al. “Unveiling DoH Tunnel: Toward Generating a Balanced DoH Encrypted Traffic Dataset and Profiling Malicious Behavior Using Inherently Interpretable Machine Learning.” Peer-to-Peer Networking & Applications, vol. 17, no. 1, Jan. 2024, pp. 507–31. EBSCOhost, https://doi.org/10.1007/s12083-023-01597-4.

APA

Niktabe, S., Lashkari, A. H., & Roudsari, A. H. (2024). Unveiling DoH tunnel: Toward generating a balanced DoH encrypted traffic dataset and profiling malicious behavior using inherently interpretable machine learning. Peer-to-Peer Networking & Applications, 17(1), 507–531. https://doi.org/10.1007/s12083-023-01597-4

Chicago

Niktabe, Sepideh, Arash Habibi Lashkari, and Arousha Haghighian Roudsari. 2024. “Unveiling DoH Tunnel: Toward Generating a Balanced DoH Encrypted Traffic Dataset and Profiling Malicious Behavior Using Inherently Interpretable Machine Learning.” Peer-to-Peer Networking & Applications 17 (1): 507–31. doi:10.1007/s12083-023-01597-4.