Network Anomaly Activity Detection Model Based on Feature Correlation Analysis
Bibliographic record
Abstract
Anomalous activity in computer networks can disrupt communication services between computers and potentially lead to attacks.Several previous studies have introduced machine learning-based anomaly detection models and have optimized them using feature selection methods.However, the feature selection process requires a correlation analysis to assess the strength of feature correlations, thereby improving the performance of the detection model.This paper proposes a new approach to detecting anomalous activity that potentially indicates malicious activity in computer networks.It aims to analyze improvements in the classification model's detection performance using correlation intersection analysis with the Pearson and Kendall correlation methods.The contribution lies in the approach of selecting correlated features using both correlation approaches, yielding the best results with eight features.In the experiment, the model uses the UNSW NB-15 public dataset and is limited to three classification methods.The Decision Tree classification method achieved optimal performance, with a detection accuracy of 96.63%, an F1-score of 94.51%, a recall of 97.96%, and a precision of 91.29%.Network administrators can utilize the proposed model to expedite the analysis of anomalous activity and integrate it with intrusion detection systems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".