A Machine Learning Based Approach to Detect Fault Injection Attacks in IoT Software Systems
Bibliographic record
Abstract
With the rapid growth of Internet of Things (IoT) applications, the security of these systems has become critical. Fault injection attacks are a type of physical attack on the hardware components of an IoT system. These attacks cause the IoT system software to behave abnormally, which the adversaries exploit. Typically, these attacks have been detected through the use of a separate hardware detection mechanism, which is expensive and itself vulnerable to attack. The purpose of this paper is to propose a machine learning based approach to detect the attacks by monitoring specific run-time software parameters in the live environment of an IoT system. The proposed approach generates a labelled dataset by injecting instruction-level faults into the software executable, which is then used to train a machine learning model that can predict whether the IoT software system is currently being affected by a fault injection attack. Using a software fault injection tool to create a labelled dataset enables the use of supervised machine learning techniques, which produce more accurate prediction results than unsupervised techniques. The machine learning model can be used in the live environment of an IoT software system to monitor specific run-time software properties in order to detect the effects of a fault injection attack on the software. Additionally, the model classifies the type of fault introduced into the software as a result of the attack, which can be used to determine the necessary corrective action.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".