Unsupervised Monitoring of Networkand Service Behaviour Using SelfOrganizing Maps
Bibliographic record
Abstract
Botnets represent one of the most destructive cybersecurity threats.Given the evolution of the structures and protocols botnets use, many machine learning approaches have been proposed for botnet analysis and detection.In the literature, intrusion and anomaly detection systems based on unsupervised learning techniques showed promising performances.This paper investigates the capability of the Self Organizing Map (SOM), an unsupervised learning technique as a data analytics system.In doing so, the aim is to understand how far such an approach could be pushed to analyze the network traffic, and to detect malicious behaviours in the wild.To this end, three different unsupervised SOM training scenarios for different data acquisition conditions are designed, implemented and evaluated.The approach is evaluated on publicly available network traffic (flows) and web server access (web requests) datasets.The results show that the approach has a high potential as a data analytics tool on unknown traffic/web service requests, and unseen attack behaviours.Malicious behaviours both on network and service datasets used
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".