Investigation of Domain Name System Attack Clustering using Semi-Supervised Learning with Swarm Intelligence Algorithms
Bibliographic record
Abstract
Domain Name System (DNS) is the Internet's system for converting alphabetic names into numeric IP addresses. It is one of the early and vulnerable network protocols, which has several security loopholes that have been exploited repeatedly over the years. The clustering task for the automatic recognition of these attacks uses machine learning approaches based on semi-supervised learning. A family of bio-inspired algorithms, well known as Swarm Intelligence (SI) methods, have recently emerged to meet the requirements for the clustering task and have been successfully applied to various real-world clustering problems. In this paper, Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Kmeans, which is one of the most popular cluster algorithms, have been applied. Furthermore, hybrid algorithms consisting of Kmeans and PSO, and Kmeans and ABC have been proposed for the clustering process. The Canadian Institute for Cybersecurity (CIC) data set has been used for this investigation. In addition, different measures of clustering performance have been used to compare the different algorithms.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".