Predicting Sensitive Information Leakage in IoT Applications Using Flows-Aware Machine Learning Approach
Bibliographic record
Abstract
<p>This thesis presents techniques for identification of vulnerable IoT applications. The techniques focus on a category of vulnerabilities that leads to sensitive information leakage which can be identified by using taint flow analysis. We analyze the source code of applications to recover tokens along their frequencies. We have developed a tool called Token2Vec, which transforms the source code tokens into vectors. If these tokens have a sink, we search for tainted flows. The tainted flows search is implemented as a tool called FlowsMiner. The tool takes far less time than static analysis counterparts. Our tool called Flow2Vec transforms the tainted flows into vectors. The machine learning algorithms are used to build models. The experiments show that the proposed approach has improved the accuracy of the prediction models for all algorithms and the best case for Corpus1 dataset is improved from 87.88% to 93.94% and for Corpus2 from 66.29% to 92.7%.</p>
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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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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 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".