Genetic optimization and hierarchical clustering applied to encrypted traffic identification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An important part of network management requires the accurate identification and classification of network traffic for decisions regarding bandwidth management, quality of service, and security. This work explores the use of a Multi-Objective Genetic Algorithm (MOGA) for both, feature selection and cluster count optimization, for an unsupervised machine learning technique, K-Means, applied to encrypted traffic identification. Specifically, a hierarchical K-Means algorithm is employed, comparing its performance to the MOGA with a non-hierarchical (flat) K-Means algorithm. The latter has already been benchmarked against common unsupervised techniques found in the literature, where results have favored the proposed MOGA. The purpose of this paper is to explore the gains, if any, obtained by increasing cluster purity in the proposed model by means of a second layer of clusters. In this work, SSH is chosen as an example of an encrypted application. However, nothing prevents the proposed model to work with other types of encrypted traffic, such as SSL or Skype. Results show that with the hierarchical MOGA, significant gains are observed in terms of the classification performance of the system.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it