Retracted: An innovation analysis of Machine Learning model to Automate Network Anomaly Detection through Time Series Analysis
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
This paper explores the potential of machine-mastering models to automate network Anomaly Detection (NAD) through Time series analysis. We employ a two-level method wherein the primary degree entails function selection thru foremost component analysis (PCA), accompanied by gadget mastering (ML) model choice from more than a few supervised studying algorithms. The second stage evaluates the overall performance of the numerous selected ML models and optimizes theirhyperparameters when necessary. Our experiments demonstrate that ML-driven computerized network Anomaly Detection can provide accurate and well-timed detection of network anomalies with little supervision and parameter tuning attempts. The outcomes of our experiments display that Random Forests and Support Vector Machines (SVMs) carry out first-rate some of the model’s grid searches, demonstrating aggressive accuracy and precision ratings from an anomaly detection perspective. We also intensely evaluate the consequences and provide insightful discussion on the possibilities and challenges surrounding using ML for automatic community Anomaly Detection.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".