Artificial Neural Network Model for Intrusion Detection System
Bibliographic record
Abstract
Artificial Intelligence (AI) breakthroughs in the last few years have accelerated dramatically as a result of the industry's vast technological use. Neural Networks (NN) is one of the most vital areas of AI, as they allow for commercial use of features that were previously not accessible via the use of computers. The Intrusion Detection System (IDS) is one of the areas in which Neural Networks are being extensively investigated to provide comprehensive computer network security and data confidentiality. During the realization of this work Artificial Neural Network (ANN) were used to shape the proposed model using a realistic CICIDS2017 dataset retrieved from the Canadian Institute for Cyber-Security (CIC) website. Following implementation and testing, it was discovered that the new model performs exceptionally well, with an average. In addition, the receiver operator characteristic curve (ROC) has a 9.999 % area under the Receiver Operator Characteristic Curve (AUC). Finally, it was discovered that the new model is exceptional and has a high level of accuracy. The new model will aid in an improved knowledge of various orders in which IDS research has been conducted. It will be useful for those working on AI-based solutions in IDS and similar domains. It is possible to enhance the new model's detection capabilities to incorporate all other lingering forms of incidents in this actual datasets, which contains all real-time and existing incidents.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".