Enhancing Anomaly-Based Intrusion Detection Systems: A Hybrid Approach Integrating Feature Selection and Bayesian Hyperparameter Optimization
Bibliographic record
Abstract
In the dynamically evolving landscape of cybersecurity, safeguarding IT infrastructures has emerged as an imperative to thwart the escalation of cyber-attacks.Anomaly-based Intrusion Detection Systems (IDS) play a pivotal role in identifying aberrant behaviours that elude conventional detection mechanisms.Nonetheless, these systems are not without their shortcomings, manifesting as elevated false alarm rates and a diminished efficacy in detecting sophisticated attacks.In response to these challenges, a hybrid approach, entailing Machine Learning (ML) techniques, was employed to augment the performance of anomaly-based IDS in terms of detection accuracy, False Positive (FP) Rate, and detection time.The approach encompassed a two-fold optimization strategy: initial feature selection predicated on feature importance derived from the XGBoost classifier, followed by Bayesian optimization (BO) for hyperparameter tuning.The optimization was conducted with respect to two objective functions, namely the ROC-AUC score and the Average Precision score, each serving to identify the optimal hyperparameters for their respective maximization.Classifiers, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Stochastic Gradient Descent (SGD), were subjected to training under configurations encompassing both the hyperparameters resultant from BO and the default hyperparameters, the latter serving as reference models.Evaluation, conducted through a multifaceted metric analysis, substantiated the superiority of the optimized models over their reference counterparts, with the optimized XGBoost models demonstrating the most commendable performance.This paradigm offers a promising avenue for enhancing detection precision and mitigating false alarms, thereby fortifying the security of computer systems.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".