A boosting genetic fuzzy classifier for intrusion detection using data mining techniques for rule pre-screening
Bibliographic record
Abstract
The purpose of the work described in this paper is to provide an intelligent intruion detection system (IIDS) that uses data mining techniques, namely classificatian and association rules mining for predicting different behaviours in networked computers. To achieve this, we propose a method based on iterative rule learning using a fuzzy rule based genetic classifier. Our approach involves two stages. First, a large number of candidate rules are generated for each class using fuzzy association rules mining and pre-screened using two rule evaluation criteria in order to reduce the fuzzy rule search space. Candidate rides, obtained after pre-screening, are used in genetic fuzzy classifier to generate rules for the classes specified in IIDS, namely Normal, PRB-probe, DOS-denial of service, U2R-user to root mad R2L- remote to local. During the second stage, boosting genetic algorithm is employed respectively for each class to find its fuzzy rules required to classify data; each time a fuzzy rule is extracted and included in the system. The boosting mechanism evaluates the weight of each data item to help the rule extraction mechanism focus more on data having relatively more weight, i.e., uucovered less by the rules extracted until the current iteration. Each extracted fuzzy rule is assigned a weight. Weighted fuzzy rules in each class are aggregated to find the vote of each class label for each data item. Experimental results demonstrate the effectiveness of the proposed approach.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".