A Survey on Network Intrusion Detection using Convolutional Neural Network
Why this work is in the frame
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Bibliographic record
Abstract
Nowadays Artificial Intelligence (AI) and studies dedicated to this field are gaining much attention worldwide. Although the growth of AI technology is perceived as a positive development for the industry, many factors are being threatened. One of these factors is security, especially network security. Intrusion Detection System (IDS) which provides real-time network security has been recognized as one of the most effective security solutions. Moreover, there are various types of Neural Networks (NN) approaches for IDS such as ANN, DNN, CNN, and RNN. This survey mainly focuses on the CNN approach, whether individually used or along with another technique. It analyses 81 articles that were carefully investigated based on a specific criterion. Accordingly, 28 hybrid approaches were identified in combination with CNN. Also, it recognized 21 evaluation metrics that were used to validate the models, as well as 12 datasets.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it