Bibliographic record
Abstract
With the ever-growing reliance on IoT-enabled sensors to age in place, a need arises to protect them from malicious activities by detecting attacks or other anomalies. In this work, we first confirm the presence of correlations between co-located sensors by statistically analyzing two public smart-home datasets and a dataset we collected from our lab. Then, we leverage the sliding window approach and supervised machine learning to develop a novel contextual-anomaly-detection model that reaches a true positive rate of 89.47% and a false positive rate of 0%. \nFurthermore, as homes become smarter with these IoT sensors, the underlying communication technology they employ becomes a target for attackers. Typically, these sensors are paired with a micro-controller that has an inbuilt communication module (e.g., Bluetooth/WiFi), to form an edge device that facilitates communication. Monitoring vitals, climate control, illumination control, fall detection, incontinence detection, pill dispensing, and several other functions are successfully addressed by these devices. The family of vulnerabilities recently found in the the Link Manager Protocol (LMP) and baseband layers of the Bluetooth Classic (BT Classic) stack called BrakTooth, poses a genuine threat to the availability of such devices. In response, our research introduces a cost-effective experimental active sniffer that captures traffic at both these layers of the BT Classic stack and utilizes supervised machine learning to detect Braktooth-based attacks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".