A Comparative Study of Incremental and Batch Machine Learning Methodologies for Network Intrusion Detection
Bibliographic record
Abstract
The exponential growth of digital networks necessitates robust intrusion detection systems (IDS) to counter evolving cyber threats effectively.Machine learning offers adaptive solutions for these challenges.This study evaluates the comparative performance of Incremental Learning and Batch Learning methodologies for IDS using two datasets, UNSW-NB15 and CI-CIDS 2017.Three machine learning algorithms-Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF)-were assessed.Results indicate that Incremental Learning outperforms Batch Learning in dynamic environments.For instance, on the CI-CIDS 2017 dataset, SVM with Incremental Learning achieved a precision of 98%, recall of 97%, and an F1-score of 97.5%, compared to Batch Learning, which obtained 95%, 93%, and 94%, respectively.These findings highlight Incremental Learning's adaptability to real-time threats despite higher computational demands.This research offers valuable insights for optimizing IDS using machine learning and proposes a framework for enhancing network security.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".