An Anomaly Based Network Intrusion Detection System Using LSTM and GRU
Bibliographic record
Abstract
Today, billions of devices are connected to the internet and the count keeps on increasing. Most of these networked devices are vulnerable to security attacks. Various types of active and passive attacks will cause severe damage to the privacy and security of millions of users. Even though we have firewalls for security but they limit the access between the networks to prevent intrusion, but they do not alert when there is an attack inside a network. The proposed method uses NSL-KDD dataset, a benchmark dataset from the Canadian Institute for Cybersecurity. We created a network intrusion detector using this dataset, which is a prediction model capable of distinguishing between “bad” connections, which are subsequently categorised into the classifications DoS, Probe, and R2L, and “good” normal connections. This study offers a Deep Learning-based Network Intrusion System. After training the model we achieve a good accuracy and precision. The highest accuracy of about 96% for classifying the Probe Attack. We got 92% accuracy for DOS attack and 88% for R2L.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 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".