Features Selection for Intrusion Detection Systems Based on Support Vector Machines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Intrusion detection systems (EDSs) deal with large amounts of data containing irrelevant and/or redundant features. These features result in a slow training and testing process, heavy computational resources, and low detection accuracy. Features selection, therefore, is an important issue in EDSs. A reduced features set improves system accuracy and speeds up the training and testing process considerably. In this paper, we propose a novel and simple method - enhanced support vector decision function (ESVDF)-for features selection. This method selects features based on two important factors: the feature's rank (weight), which is calculated using support vector decision function (SVDF), and the correlation between the features, which is determined by either the forward selection ranking (FSR) or backward elimination ranking (BER) algorithm. Our method significantly decreases training and testing times without loss in detection accuracy. Moreover, it selects the features set independently of the classifier used. We have examined the feasibility of our approach by conducting several experiments using the DARPA dataset. The experimental results indicate that the proposed algorithms can deliver satisfactory results in terms of classification accuracy, training time, and testing time.
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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 | 0.000 | 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