Word encoding for word-looking DGA-based Botnet classification
Bibliographic record
Abstract
There are two main types of domain name-generating algorithms (DGAs) – random-looking and word-looking. While existing methods can effectively distinguish between the two types of DGAs with high accuracy, classifying different types of word-looking DGAs has proven to be challenging, as they are often mistaken for legitimate domains. To address this issue, previous methods used character encoding with long short-term memory networks (LSTM) or convolutional neural networks (CNN) to model the character distribution of different word-looking DGAs. Since most word-looking DGAs are constructed using various dictionaries, we propose using word encoding instead of character encoding. Word encoding can provide a better characterization as it is based on the usage of different words in the dictionaries and their associations. Experimental results show that the classification accuracy for word-based DGAs increases by more than 7% (from 87% to 94%) using word encoding as compared to character encoding.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".