A Framework for Synthetic Agetech Attack Data Generation
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
To address the lack of datasets for agetech, this paper presents an approach for generating synthetic datasets that include traces of benign and attack datasets for agetech. The generated datasets could be used to develop and evaluate intrusion detection systems for smart homes for seniors aging in place. After reviewing several resources, it was established that there are no agetech attack data for sensor readings. Therefore, in this research, several methods for generating attack data were explored using attack data patterns from an existing IoT dataset called TON_IoT weather data. The TON_IoT dataset could be used in different scenarios, but in this study, the focus is to apply it to agetech. The attack patterns were replicated in a normal agetech dataset from a temperature sensor collected from the Information Security and Object Technology (ISOT) research lab. The generated data are different from normal data, as abnormal segments are shown that could be considered as attacks. The generated agetech attack datasets were also trained using machine learning models, and, based on different metrics, achieved good classification performance in predicting whether a sample is benign or malicious.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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