Bibliographic record
Abstract
BLE (Bluetooth Low Energy) is being heavily deployed in many devices and IoT (Internet of Things) smart applications of various fields, such as medical, home automation, transportation and agriculture. It has transformed the classic Bluetooth into a technology that can be embedded into resource constrained devices running on a cell coin battery for months or years. Most BLE devices that are sold in the market use the Just Works pairing mode to establish a connection with peer devices. This mode is so lightweight that it leaves the implementation of security to application developers and device manufacturers. Unfortunately, as the market does not want to pay for security, a number of vulnerable smart devices are strolling around in the market. In this paper, we discuss how Bluetooth devices that use the Just Works pairing mode can be exploited to become nonoperational. We conduct a case study on three different Bluetooth smart devices. We show how these devices can be attacked and abused to not work properly. We also present a vulnerability that is due to the behavior of BLE smart devices and the Just Works pairing mode. This vulnerability can be exploited to generate an attack that affects BLE availability. We propose a solution to mitigate the attack.
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".