Utilising Artificial Intelligence in Enhancing Zero-Day Attacks Detection
Bibliographic record
Abstract
With the alarming increase in zero-day attacks and the limitations facing current traditional intrusion detection systems, enhancing zero-day attack detection is paramount. This research proposes the use of artificial intelligence algorithms in improving the detection of zero-day attacks. Three supervised machine learning algorithms were employed to evaluate the detection capability of machine learning models compared to traditional intrusion systems. The study was conducted by assessing the performance of Snort, an open-source intrusion detection/prevention system, Decision Tree Classifier, K-Neighbor Classifier, and Random Forest Classifier on the Canadian Institute for Cybersecurity Intrusion Detection Evaluation Dataset (CIC-IDS2017). To improve the performance of the machine learning algorithms, the features were standardised, the dataset’s dimension reduced, and sampling techniques used in attaining a balanced dataset class. The Decision Tree Classifier, K-Neighbor Classifier, and Random Forest Classifier had an accuracy of 0.904, 0.929, and 0.919 respectively. The Decision Tree Classifier had the fastest runtime of 0.006 seconds and the highest processing rate, processing 150,000 entries per second.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".