A Proposed Intrusion Detection System Based on an Improved Random Forest Using a Double Feature Selection Method
Bibliographic record
Abstract
Cyber-attacks today are a source of great concern due to the increase in the use of the Internet in many areas, which has allowed increasing intrusion on networks and attempts to damage systems and others.Therefore, to stay up with the evolution of cyber-attacks, intrusion detection systems must be constantly improved.Intrusion detection system is a technique that may be applied to track both known and unidentified breaches before one of them damages network hardware.One of the very important things that has a big role in the strength of the system is the selection of good features in training the system.In this research paper, intrusion detection systems are proposed based on reducing and selecting features through the use of a "double feature selection" with the random forest algorithm.Experiments were performed on a data set NSL-KDD (it dataset from the Canadian Institute for Cybersecurity).By evaluating the performance, a system accuracy of 0.9981, a training time of 3.47 sec and a detection time of 0.24 sec were obtained.The proposed work was compared with related work using the same algorithm and dataset.The system proved superior to many of the proposed systems in terms of accuracy of the system, recall, precision, the time spent in training the system and the time of detection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".