Intrusion Detection Method Using Protocol Classification and Rough
Bibliographic record
Abstract
In order to improve the efficiency of support vector intrusion detection, we first do protocol Classification for the intrusion data, then refine its characteristic by rough set reduction. By using these procedures, we propose an intrusion detection method using protocol classification and rough set based support vector machine. The method is divided into training and testing processes. In the process of training, we first do protocol classification for the training data, and then do rough set refinement. The refined characteristics are stored as the pre-defined process, and finally the usage of support vector machine for data reduction training, the training model will be stored in accordance with the agreement. In the testing process, the data is classified according to protocol classification and then start the characteristics reduction procedure according to protocol classification. Finally, make a decision using the Support Vector Machines that corresponding to the agreement. The experimental results based on KDDCUP'99 data show that the method is the method is faster and the detection accuracy is comparable compared with the SVM without using protocol classification and using all characteristic.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".