PEASE: A PUF-Based Efficient Authentication and Session Establishment Protocol for Machine-to-Machine Communication in Industrial IoT
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
Machine-to-machine (M2M) communication is one of the critical technologies of the industrial Internet of Things (IoT), which consists of sensors, actuators at the edge, and servers. In order to solve the security and availability problems regarding communication between edge devices with constrained resources and servers in M2M communication, in this study we proposed an authentication and session establishment protocol based on physical unclonable functions (PUFs). The scheme does not require clock synchronization among the devices, and it circumvents the situation where the authentication phase has to use a high computational overhead fuzzy extractor due to PUF noise. The protocol contains two message interactions, which provide strong security and availability while being lightweight. The security modelling is based on CPN Tools, which verifies security attributes and attack resistance in the authentication phase. After considering the design of the fuzzy extractor and scalability, the proposed scheme significantly reduces the computational overhead by more than 93.83% in the authentication phase compared with other schemes using PUFs. Meanwhile, under the guarantee of availability, the communication overhead is maintained at a balanced and reasonable level, at least 19.67% lower than the solution using XOR, hashing, or an elliptic curve.
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.
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 | 0.000 |
| 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 | 0.001 | 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