OLIDA-IDS: Online Learning with Integrated Domain Adaptation for Intrusion Detection Systems
Bibliographic record
Abstract
Modern networks are dynamic and heterogeneous, leading to significant challenges for Intrusion Detection Systems (IDS) due to data drift and concept drift phenomenons. Traditional Machine Learning (ML) models trained on a source domain often suffer performance degradation when deployed in a target domain, while online learning models require substantial data to adapt effectively. To address these limitations, we propose a hybrid methodology that integrates online learning with domain adaptation for intrusion detection (OLIDA-IDS). OLIDA-IDS begins with a static random forest model trained on the source domain, employs a Stacked Marginalized Denoising Autoencoder (sMDA) for unsupervised domain adaptation to align feature distributions, and transitions to an Adaptive Random Forest (ARF) for online learning as target data becomes available incrementally. Experimental results demonstrate that OLIDA-IDS achieves 99.59% accuracy in the target network environment, outperforming static, semi-supervised, and incremental learning approaches. Key contributions include the novel integration of sMDA for domain adaptation, a weighted-accuracy technique for seamless transition between static and online models, and the use of ARF for superior resilience in evolving network threats. This work bridges the gap between domain adaptation and online learning, offering a feasible solution for real-world IDS deployment in dynamic environments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".