New class‐dependent feature transformation for intrusion detection systems
Bibliographic record
Abstract
ABSTRACT Intrusion Detection Systems (IDS) mainly focus on the original features extracted from the communications networks without complex pre‐processing. In this paper, we propose new methods for class‐dependent feature transformation to improve the accuracy of the IDS. In the previously known class‐dependent feature transformation methods, the mapping process is accomplished by employing separate mapping matrices for each class of the dataset. In the training phase, samples of each class is mapped using only the corresponding matrix, whereas, in the test phase, each sample is mapped using all transformation matrices. This may lead to inaccuracy in classification. We modify the training and test phases of the class‐dependent methods to extract more information from the dataset in the training phase that the other class‐dependent methods ignore. Unlike the previously known class‐dependent methods, the training and test phases of our proposed methods are very similar. We evaluate the performance of the proposed methods by measuring Mutual Information, and Maximum‐Relevancy Minimum‐Redundancy Information on a benchmark dataset for intrusion detection, namely NSL‐KDD dataset, and on three different types of classifiers: distance‐based, neural network‐based, and decision tree‐based classifiers. The experimental results demonstrate that the classifiers trained on the dataset transformed by our proposed feature transformation methods are more accurate in detecting intruders. In all experiments, the proposed methods perform better than their peers in increasing the classifier accuracy and reducing the false alarm of the detection process. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".