UAV Security logs processing using Machine Learning Algorithms
Bibliographic record
Abstract
In this paper, we propose an evaluation of Machine Learning algorithms for security logs collected from an Intrusion Detection System (IDS) integrated in a Unmanned Aerial Vehicles (UAV) architecture. Machine Learning (ML) methodologies in the analysis of security logs presents a promising strategy for enhancing the capabilities of cybersecurity experts in addressing cyber threats. Currently, the utilization and the processing of system security logs presents a significant challenge for organizations due to its time-consuming nature and the extensive human resources required. In this study, we utilized UAV security logs generated by a publicly accessible log simulator. The investigation is focused on evaluating the efficiency of five ML algorithms: Unsupervised Support Vector Machines, Logistic Regression, Random Forest, K-nearest neighbors (KNN) and Gradient Boosting, utilizing a simulated dataset that has been pre-processed. Five use cases were examined utilizing security logs content, and our analysis revealed that Logistic Regression demonstrated exceptional performance in all the studied areas.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".