A tree growth based forward feature selection algorithm for intrusion detection system on convolutional neural network
Bibliographic record
Abstract
With the rapid advancement of networking technologies, security system has become increasingly important to academics from several sectors. Intrusion detection (ID) provides a valuable protection by reducing the human resources required to keep an eye on intruders, improving the efficiency of detecting the various attacks in networks. Machine learning and deep learning are two key areas that have recently received a lot of attention, with a focus on improving the precision of detection classifiers. Using defense anvance research project agency (DARPA”98) datasets, a number of academics and research have developed intrusion detection systems. This paper discusses various approaches developed by different researchers, including scale-hybrid-IDS-AlertNet (SHIA), forward feature selection algorithm (FFSA), modified- mutual information feature selection (MMIFS), deep neural network (DNN), and the holes that remain to be filled, highlighting areas where these procedures can be improved, also are addressed and the proposed approach improved deep convolutional neural network (IDCNN) is compared with existing approach.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".