Detecting DNS over HTTPS Traffic Using Ensemble Feature-based Machine Learning
Bibliographic record
Abstract
Cybercriminals use various tools, including malware, phishing, ransom ware, and denial of service, among others, to conduct cyber-attacks. Attackers also use the Domain Name System (DNS) as an unpredictable cybercrime medium. To prevent this kind of attack, the Hypertext Transfer Protocol Secure (HTTPS) plays a significant role that secures computer communication over the network. An alternative solution has been proposed by security organizations where the DNS request travel over HTTPS, and the concept is known as DNS over HTTPS (DoH). However, the attackers use this new protocol to inject their data in an encrypted way which is undetectable by firewalls and other methods. The Canadian Institute of Cyber Security recently published a data set CIRA-CIC-DoHBrw-2020\cite{8} consisting of malicious and benign DoH traffic. This paper proposes a machine learning solution based on an ensemble feature selection technique to classify the DoH traffic. We experimented with several machine learning models feeding different feature sets extracted from different feature selection algorithm. According to our analysis, the ensemble feature-based machine learning model outperforms the other models based on the individual feature set.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".