Robust Defenses Against Adversarial Machine Learning in IoT Security
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.Words cannot express my gratitude to the chair of my committee for the invaluable patience and feedback.I also could
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".