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

Crossref DOI link: https://doi.org/10.1007/s12083-023-01597-4

Published Online: 2023-12-23

Published Print: 2024-01

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Niktabe, Sepideh https://orcid.org/0009-0005-2038-1298
Lashkari, Arash Habibi https://orcid.org/0000-0002-1240-6433
Roudsari, Arousha Haghighian https://orcid.org/0000-0001-6386-7806
Funding

Funding for this research was provided by:

Canada Research Chair - Tier II (CRC-2021-00340)

Natural Sciences and Engineering Research Council of Canada — NSERC (RGPIN-2020-04701)
License Information

Text and Data Mining valid from 2023-12-23

Version of Record valid from 2023-12-23
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Article History

Received: 15 September 2023

Accepted: 14 November 2023

First Online: 23 December 2023

Declarations

:

: This article does not contain any studies with human participants or animals performed by any of the authors.

: Permitted.

: The authors declare no competing interests.

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