Hybrid Relabeled Model for Network Intrusion Detection
Bibliographic record
Abstract
With growing web communications throughout the Internet, the need for better security protection has been intensified. Intrusion detection is important for identifying malicious activities and cyber-crimes. Recently, UNSW-NB15 dataset has been popular in the research community for network intrusion detection system as it is publicly available labelled dataset which has a hybrid of real normal and contemporary synthesized attack activities of the network traffic. The goal of this paper is to relabel an unsupervised labelled data using a hybrid approach based on supervised learning. The methodology for training model in this work includes: (i) feature selection to remove redundant and highly correlated features, (ii) clustering the training dataset to create referential labels based on the size of cluster by using selected features, (iii) creating supervised learning model using ensemble classifiers with the generated referential labels, and (iv) testing individual data-point in doubt using the generated learning model. Our results show 81.29% accuracy compared to the original labels. Further, the proposed ensemble technique using LogitBoost and Random Forest algorithms produces 90.33% accuracy with the original labels, and 99.99% accuracy with the new labels for both training and testing dataset.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".