Managing cybersecurity break-ins using bluetooth low energy devices to verify attackers: A practical study
Bibliographic record
Abstract
We present a novel solution in tracking the behaviour of an attacker and limiting their ability to compromise a cybersecurity system. The solution is based on combining a decoy with a real system, where a BLE controller will be placed in the middle, acting like a fob that opens and closes the access of the server's BLE. If the first server wants to communicate with the second server, the BLE must be activated by the BLE controller in order for both servers to communicate with one another. This is a relatively low-cost solution and our aim is to lower the interruption to the live system, capture the attacker's position, and limit the damages the attacker can do to a live system. A second related goal is to lower the attacker's opportunity to detect that they are being monitored. A third goal is to gather evidence of the attacker's actions that can be used for further investigation. This work is significant in that it is implemented within a real physical system for testing and evaluation using Raspberry PI and Arduino boards to replicate servers that communicate wirelessly. Several custom programs are written from scratch to monitor the attacker's behaviour, and the use of Bluetooth Low Energy to verify users. When the device was disassembled, all of the Raspberry PI, which run the Linux servers, were discontinued and unable to communicate with other 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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".