Applying ML Algorithms to improve traffic classification in Intrusion Detection Systems
Bibliographic record
Abstract
Traditional intrusion detection systems may have higher false-positive and false-negative rates against new malicious traffic vectors. Also, in the case of anomaly-based IDS can be bypassed by generating network traffic intelligently. The capability of machine learning algorithms in capturing complex behaviors and patterns made them increasingly popular in solving classification/detection problems. The major objective of this paper is to suggest an efficient IDS model by studying various supervised machine learning algorithms on the classification problem. For this purpose, the known NSLKDD dataset was used as a source of diverse feature columns for the model The transformed data is modeled to classify network traffic into normal or attack using machine learning algorithms SVM, KNN, neural network and ensemble learning in which KNN and SVM achieved 98 and 97% accuracy. These models can be used to differentiate anomalous traffic in intrusion systems and maybe useful as a replacement for traditional rule-based detection systems. Click here for dataset and code of IDS models.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".