Secure Authentication and Key Agreement Protocol for Tactile Internet-based Tele-Surgery Ecosystem
Why this work is in the frame
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Bibliographic record
Abstract
With the recent advancements in wireless communications, Tactile Internet (TI) has witnessed a major blow. TI is considered the next big evolution that will provide real-time control in industrial setups, particularly in the domain of tele-surgery. However, in remote-surgery ecosystems the transmission of data is prone to different attack vectors. Thus, to realize the true potential of secure tele-surgery under the umbrella of TI, it is required to design a secure authentication and key agreement protocol for tele-surgery. In this paper, we present an effective and secure mutual authentication and session establishment protocol for TI-driven remote surgery setups. The designed protocol enables secure communications between the surgeon, robotic arm, and the trusted authority (TA); where the protocol leverages the advantages of Elliptic Curve Cryptography (ECC) and biometrics. The protocol operates along the following three phases: i) setup phase, ii) registration phase, and iii) mutual authentication and key agreement phase. During the third phase, the surgeon and the robotic arm mutually authenticate each other with the help of the TA. Further, the security features of the designed protocol have been established using formal and informal means. The obtained results indicate the resiliency of the protocol against offline password guessing attacks, replay attacks, impersonation attacks, man-in-the-middle attacks, denial of service attacks, etc.
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 | 0.000 | 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 | 0.000 | 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