Security and Privacy Threats for Bluetooth Low Energy in IoT and Wearable Devices: A Comprehensive Survey
Bibliographic record
Abstract
Bluetooth Low Energy (BLE) has become the de facto communication protocol for the Internet of Things (IoT) and smart wearable devices for its ultra-low energy consumption, ease of development, good enough network coverage, and data transfer speed.Due to the simplified design of this protocol, there have been lots of security and privacy vulnerabilities.As billions of health care, personal fitness wearable, smart lock, industrial automation devices adopt this technology for communication, its vulnerabilities should be dealt with high priority.Some segregated works on BLE were performed focusing on various vulnerabilities, such as the insecure implementation of encryption, device authentication, user privacy, etc.However, there has been no comprehensive survey on the security vulnerabilities of this protocol.In this survey paper, we present a comprehensive taxonomy for the security and privacy issues of BLE.We present possible attack scenarios for different types of vulnerabilities, classify them according to their severity, and list possible mitigation techniques.We also provide case studies regarding how different vulnerabilities can be exploited in real BLE devices.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".