Comparative Study of Supervised Machine Learning Techniques for Intrusion Detection
Why this work is in the frame
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Bibliographic record
Abstract
Intrusion detection is an effective approach for dealing with various problems in the area of network security. This paper presents a comparative study of using supervised probabilistic and predictive machine learning techniques for intrusion detection. Two probabilistic techniques Naive Bayes and Gaussian and two predictive techniques decision tree and random forests are employed. Different training datasets constructed from the KDD99 dataset are employed for training. The ability of each technique for detecting four attack categories (DoS,Probe,R2L and U2R) have been compared. The statistical results to show the sensitivity of each technique to the population of attacks in a dataset have also been reported. We compare the performance of the techniques and also investigate the robustness of each technique by calculating their standard deviations with respect to the detection rate of each attack category.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it