Assessing the impact of bag‐of‐words versus word‐to‐vector embedding methods and dimension reduction on anomaly detection from log files
Bibliographic record
Abstract
Abstract In terms of cyber security, log files represent a rich source of information regarding the state of a computer service/system. Automating the process of summarizing log file content represents an important aid for decision‐making, especially given the 24/7 nature of network/service operations. We perform benchmarking over eight distinct log files in order to assess the impact of the following: (1) different embedding methods for developing semantic descriptions of the original log files, (2) applying dimension reduction to the high‐dimensional semantic space, and (3) assessing the impact of using different unsupervised learning algorithms for providing a visual summary of the service state. Benchmarking demonstrates that (1) word‐to‐vector embeddings identified by bidirectional encoder representation from transformers (BERT) without “fine‐tuning” are sufficient to match the performance of Bag‐or‐Words embeddings provided by term frequency‐inverse document frequency (TF‐IDF) and (2) the self‐organizing map without dimension reduction provides the most effective anomaly detector.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".