Extending C2 Traffic Detection Methodologies: From TLS 1.2 to TLS 1.3-enabled Malware
Bibliographic record
Abstract
As the Internet evolves from TLS 1.2 to TLS 1.3, it offers enhanced security against network eavesdropping for online communications. However, this advancement also enables malicious command and control (C2) traffic to more effectively evade malware detectors and intrusion detection systems. Among other capabilities, TLS 1.3 introduces encryption for most handshake messages and conceals the actual TLS record content type, complicating the task for state-of-the-art C2 traffic classifiers that were initially developed for TLS 1.2 traffic. Given the pressing need to accurately detect malicious C2 communications, this paper examines to what extent existing C2 classifiers for TLS 1.2 are less effective when applied to TLS 1.3 traffic, posing a central research question: is it possible to adapt TLS 1.2 detection methodologies for C2 traffic to work with TLS 1.3 flows?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".