A Support Vector Machine Cost Function in Simulated Annealing for Network Intrusion Detection
Bibliographic record
Abstract
This paper proposes a computationally intelligent algorithm for extracting relevant features from a training set.An optimal subset of features is extracted from training examples of network intrusion datasets.The Support Vector Machine (SVM) algorithm is used as the cost function within the thermal equilibrium loop of the Simulated Annealing (SA) algorithm.The proposed fusion algorithm uses a combinatorial optimization algorithm (SA) to determine an optimal feature subset for a classifier (SVM) for the classification of normal and abnormal packets (possible intrusion threats) in a network.The proposed methodology is analyzed and validated using two different network intrusion datasets and the performance measures used are; detection accuracy, false positive and false negative rate, Receiver Operation Characteristics (ROC) curve, area under curve value and F1-score.A comparative analysis through empirically determined measures show that the proposed SA-SVM based model outperforms the general SVM and decision tree-based detection schemes based on performance measures such as detection accuracy, false positive and false negative rates, area under curve value and F1-score.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".