Intrusion Detection in the IoT under Data and Concept Drifts: Online Deep Learning Approach
Bibliographic record
Abstract
Although the existing machine learning-based intrusion detection systems in the Internet of Things (IoT) usually perform well in static environments, they struggle to preserve their performance over time, in dynamic environments. Yet, the IoT is a highly dynamic and heterogeneous environment, leading to what is known as data drift and concept drift. Data drift is a phenomenon which embodies the change that happens in the relationships among the independent features, which is mainly due to changes in the data quality over time. Concept drift is a phenomenon which depicts the change in the relationships between input and output data in the machine learning model over time. To detect data and concept drifts, we first propose a drift detection technique that capitalizes on the Principal Component Analysis (PCA) method to study the change in the variance of the features across the intrusion detection data streams. We also discuss an online outlier detection technique that identifies the outliers that diverge both from historical and temporally close data points. To counter these drifts, we discuss an online deep neural network that dynamically adjusts the sizes of the hidden layers based on the Hedge weighting mechanism, thus enabling the model to steadily learn and adapt as new intrusion data come. Experiments conducted on an IoT based intrusion detection dataset suggest that our solution stabilizes the performance of the intrusion detection on both the training and testing data compared to the static deep neural network model, which is widely used for intrusion detection.
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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 | 0.007 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".