Enhanced Adversarial Domain Adaptation for Intrusion Detection Systems
Bibliographic record
Abstract
The increasing sophistication of cyber threats demands robust and adaptive Intrusion Detection Systems (IDS) capable of generalizing across diverse network environments. However, traditional AI-driven IDS models suffer from performance degradation when deployed in unseen domains due to domain shift discrepancies in data distributions caused by varying network configurations, attack patterns, or data collection methods. While unsupervised domain adaptation has recently been applied to address domain shift, its use in IDS remains limited and often lacks adaptation to the unique challenges of network data. To bridge this gap, we propose an Enhanced Adversarial Domain Adaptation (E-ADDA) Framework for IDS, designed to align feature representations between source and target domains, enhancing model generalizability. Our framework is rigorously evaluated on three publicly available IDS datasets, demonstrating significant improvements in key metrics such as accuracy, F1 score, and loss compared to existing domain adaptation methods. The results highlight the viability of adversarial domain adaptation in improving IDS resilience against zero-day attacks and evolving threats, offering a promising direction for real-world cybersecurity applications.
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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 | 0.005 |
| 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 | 0.001 |
| 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".