A Framework for Real-Time Continual Learning Federated Intrusion Detection Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Intrusion Detection and Prevention Systems (IDS/IPS) are vital components of security architecture for protecting the networks against cyberattacks. Traditional IDS/IPS rely on static rules and user configurations, which make them less effective against growing threats. Modern studies have integrated Artificial Intelligence (AI) and Machine Learning (ML) to IDS to improve the accuracy and detection speed. However, such AI/ML based systems still face many issues, which include reliance on the outdated datasets, almost no handling of zero-day attacks, lack of interpretability, and privacy concerns. This paper studies recent AI/ML based IDS/IPS works to identify key shortcomings, and then proposes a real-time, continually learning, federated IDS framework with integrated explainable AI. The proposed framework design addresses the adaptability, privacy, and trustability aspects, which can be used to build more resilient network defense systems
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it