Ensemble Technique for Intruder Detection in Network Traffic
Bibliographic record
Abstract
Due to increasing incidents of cyber-attacks, building effective intrusion detection systems are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge.However, most of the conducted studies rely on static and one-time dataset where all the changes monitored are based on the dataset used.As network behaviors and patterns change and intrusions evolve, thus it has very much become necessary to move away from static and one-time dataset toward more dynamically configurable classifiers.The Current researches show that different classifiers provide different results about the patterns to be classified.These different results combined together (aka ensemble) yields better performance than individual classifiers.In this paper we have used a hybrid ensemble intrusion detection system consisting of a Misuse Binary Tree of Classifiers as the first stage and an anomaly detection model based upon SVM Classifier as the second stage.The Binary Tree consists of several best known classifiers specialized in detecting specific attacks at a high level of accuracy.Combination of a Binary Tree and specialized classifiers will increase accuracy of the misuse detection model.The misuse detection model will detect only known attacks.In-order to detect unknown attacks, we have an anomaly detection model as the second stage.SVM has been used, since it's the best known classifier for anomaly detection which will detect patterns that deviate from normal behavior.The proposed hybrid intrusion detection has been tested and evaluated using KDD Cup '99, NSL-KDD and UNSW-NB15 datasets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".