Anonymous Lightweight Device-to-Device Continuous Authentication Protocol for IoT
Bibliographic record
Abstract
Continuous authentication is proposed as an alternative to the current methods. It is one of the core tenets of the Zero Trust Architecture model. Several continuous authentication protocols have been proposed for resource-constrained Internet of Things devices. However, the vast majority of them are designed for human/machine interaction, or use computationally expensive methods such as device fingerprinting and artificial intelligence. Others are designed for device-to-gateway communication scenarios. In this study, we present a new anonymous device-to-device continuous authentication protocol for resource-constrained Inter-net of Things devices. It is lightweight and uses simple cryptographic primitives such as XOR and HMAC. Our protocol generates dynamic session keys based on cubic spline polynomials and message transmission delays. Additionally, new temporary identities are generated for each continuous authentication phase. We successfully conducted an informal security analysis and an automated verification with Scyther to validate the security properties of the protocol and its robustness against known attacks. A performance comparison with recent works showed that the protocol requires less computation overhead, and its higher communication cost is justified by the values exchanged to preserve the anonymity of the 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".