On efficiency enhancement of the correlation-based feature selection for intrusion detection systems
Bibliographic record
Abstract
The dramatic increase in the network traffic data has become a major concern in security systems. Intrusion detection systems (TDSs), as common widely used security systems for communication networks, are not an exception. An IDS monitors the network traffic to detect attacks through classifying the network traffic data into normal and abnormal classes. Due to the high dimensionality of the network traffic data, it is not always feasible for an IDS to detect intrusions quickly and accurately. Feature selection emerges as a necessary step in designing an IDS to overcome its shortcoming and enhance its performance through the reduction of its complexity and acceleration of the detection process. To this end, in this paper, we address the problem of dimensionality reduction by proposing an efficient feature selection algorithm that considers the correlation between a subset of features and the behavior class label. Correlation-based feature selection (CFS) and symmetrical uncertainty (SU) are the two correlation metrics used to measure the dependency level between features and class labels, and among features. Experimental results on NSL-KDD dataset shows that the proposed approach with fewer features, significantly outperforms the existing schemes in terms of the training time, time taken to build the model, while it preserves or increases the system accuracy. In addition, the efficiency of the proposed feature selection technique is tested on different classification algorithms and comparison results indicates that J48 classifier with the highest accuracy and precision values and lowest miss rate and false alarm rate values, performs better with the proposed feature selection technique.
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".