Securing Encrypted 6G Traffic: An Edge-Optimized AI Framework for Attack Detection
Bibliographic record
Abstract
In today’s networks, encryption is ubiquitous—protecting data privacy while simultaneously hindering security monitoring. This research introduces a privacy-preserving framework that achieves 99.2% accuracy in detecting brute-force attacks in encrypted 6G traffic without decryption. We compare supervised (CNN) and unsupervised (autoencoder) models operating exclusively on flow-level metadata such as packet timing, size distribution, and protocol behavior. The CNN classifier significantly outperforms the autoencoder and can be optimized (0.7MB) for edge deployment, enabling terabit-speed monitoring with sub-millisecond latency when distributed across 6G networks. Our approach resolves the security-privacy tension by maintaining encryption while effectively identifying malicious activities, making it ideal for future 6G environments where both privacy and computational efficiency are critical.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".