Anonymous IoT Mutual Inter-Device Authentication Scheme Based on Incremental Counter (AIMIA-IC)
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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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".