HEAL: Heterogeneous Ensemble and Active Learning Framework
Bibliographic record
Abstract
Network Intrusion Detection is a well-known, relevant problem that has gained even more interest due to the exponential growth of technologies, systems and volume of data. The constantly evolving attacks require a continuous effort towards the development of novel and robust detection solutions. In this paper we propose a Heterogeneous Ensemble and Active Learning (HEAL) system, a novel tool that incorporates the implementation of a dynamic heterogeneous ensemble model with active learning capabilities. This ensures a solution that: i) adapts to changes in data through time, ii) remains robust providing good performance, iii) handles a continuous flow of data, and iv) requires less human intervention when compared against pure active learning solutions. HEAL system uses multiple individual base models to build a heterogeneous ensemble learner that adapts to the specific data characteristics. Then, active learning is applied to the ensemble so that it is retrained and re-evaluated with respect to time and new instances. Instances where the model has a low confidence are labeled by a domain expert. A new model is retrained with these instances and its performance is evaluated. The deployed model is replaced when the new model exhibits performance advantages. Finally, an experimental comparison of the performance at different stages is carried out in a case study using the well-known NSL-KDD data set. In this study we show the advantages of using HEAL system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".