Robust Defenses Against Adversarial Machine Learning in IoT Security
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
The cybersecurity of the Internet of Things (IoT) has been poised to benefit from Artificial Intelligence (AI).Advances such as AI-based Intrusion detection systems for IoT have shown promising results.However, these advances have been set back by the rise of Adversarial Samples.Adversarial Samples are specially crafted data samples that are designed to mislead an AI model into making a wrong prediction.When subjected to Adversarial Samples, AI models that have been optimally trained to make accurate predictions, will produce incorrect results.In this thesis document, we explore the reasons behind the vulnerability of AI models to Adversarial Samples.We also propose novel methods for addressing the challenge of Adversarial Samples in the specific context of cybersecurity applications for IoT.I would like to express my deepest appreciation to my PhD supervisors Dr. M. Omair Shafiq and Dr. Ashraf Matrawy for their guidance throughout the journey.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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