Mitigating Adversarial Attacks against IoT Profiling
Bibliographic record
Abstract
Internet of Things (IoT) applications have been helping society in several ways. However, challenges still must be faced to enable efficient and secure IoT operations. In this context, IoT profiling refers to the service of identifying and classifying IoT devices’ behavior based on different features using different approaches (e.g., Deep Learning). Data poisoning and adversarial attacks are challenging to detect and mitigate and can degrade the performance of a trained model. Thereupon, the main goal of this research is to propose the Overlapping Label Recovery (OLR) framework to mitigate the effects of label-flipping attacks in Deep-Learning-based IoT profiling. OLR uses Random Forests (RF) as underlying cleaners to recover labels. After that, the dataset is re-evaluated and new labels are produced to minimize the impact of label flipping. OLR can be configured using different hyperparameters and we investigate how different values can improve the recovery procedure. The results obtained by evaluating Deep Learning (DL) models using a poisoned version of the CIC IoT Dataset 2022 demonstrate that training overlap needs to be controlled to maintain good performance and that the proposed strategy improves the overall profiling performance in all cases investigated.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".