Applying Variable Coe_cient functions to Self-Organizing Feature Maps for Network Intrusion Detection on the 1999 KDD Cup Dataset
Why this work is in the frame
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Bibliographic record
Abstract
Self-Organizing Feature Maps (SOFM's) can be a valuable element in a network intrusion detection system. When classification is performed on a segment of network tra_c, the usual method for class determination is selecting the class which has the smallest measurement of the Euclidean distance from the multi-dimensional network tra_c sample to the class’ multi-dimensional prototype. This minimum distance is calculated with equivalent weights for each dimension of data in the network tra_c sample. In this paper we explore the possibility of applying di_erent randomly generated weightings to each dimension of data in the network tra_c sample to increase positive classifications of the network sample data provided by the 1999 KDD Cup Dataset. We show that there is improvement, and recommend that further studies be done in choosing the right evolutionary functions to help modify the hotspots and achieve better results.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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