Intrusion Detection Algorithm of Artificial Immune Based on Decision Tree and Genetic Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Aiming at solving the problem that there were large amounts of ineffective antibodies and the antibodies were lack of diversity in the traditional negative selection algorithm,this paper designed intrusion detection algorithm of artificial immune based on decision tree and genetic algorithm.The decision tree and the genetic algorithm were introduced into the traditional negative selection algorithm,the affinity between antibody and antigen was calculated using decision tree,the new formula of fitness was raised.The diversity of antibody set was measured by concentration of antibody,and the high concentration antibodies were replaced by the low concentration antibodies to achieve the diversity of the antibody set.When the quantity of the antibody set was kept at a constant,the nonself set space could be covered as large as possible so as to enhance the capability of the antibody set.
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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