Anonymous IoT Mutual Inter-Device Authentication Scheme Based on Incremental Counter (AIMIA-IC)
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
Cyber attackers are shifting their attention from traditional computers to IoT devices for malignant activities like exposing smart homeowner private information and/or to launch botnet attacks. Like for conventional networks, the security of IoT networks rests on how properly the authentication process is done. However, unlike conventional networks, IoT infrastructure faces an uphill battle in deploying and operating strong authentication schemes because of inherent limitations on the underlying storage and computation capability. In this paper, we propose a new anonymous mutual Inter-device authentication protocol based on transient identities, incremental counter and temporary secret keys for IoT. The proposed protocol is based on symmetric cryptography and somehow follows the ZigBee protocol. It allows IoT devices to anonymously and mutually authenticate in an unlinkable and untraceable manner, and implements essential security requirements for IoT devices. By analyzing the protocol, we evaluate and demonstrate its efficiency and its relatively limited computational and storage overhead. Furthermore, the security of the protocol is assured through informal security analysis and formally by using the automated validation of Internet security protocols and applications (AVISPA) toolkit.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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