Zero-Day Device Identification for IoT Security through Optimized Incremental Learning and Drift Adaptation
Bibliographic record
Abstract
Zero-day attacks exploit IoT devices’ vulnerabilities using unknown compromised devices as main attack vectors. Therefore, immediate identification of new devices that join networks is critical. This paper introduces an optimized incremental learning framework that combines XGBoost and sliding-window drift detection for zero-day device identification, optimized with bacterial foraging optimization (BFOA) to select the best window size and hyperparameter to achieve reduced time. This method addresses catastrophic forgetting in streaming IoT environments while preserving detection accuracy for unseen devices. The system utilizes sliding-window-based concept drift detection and adaptive model updates to enable continuous learning without performance degradation. Extensive evaluation on four realworld IoT traffic datasets demonstrates superior performance, achieving 99. 11% precision in device detection with computational efficiency suitable for real-time deployment. The adaptive framework ensures model adjustment to varying patterns in the network of IoT devices, optimizing both cost and efficiency while detecting previously unseen device type with up to 99% precision.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".