Learning Automata Based Secure Multi Agent RFID Authentication System
Bibliographic record
Abstract
Radio frequency identification wireless sensing technology widely adopted and developed from last decade and has been utilized for monitoring and autonomous identification of objects. However, wider utilization of RFID technologies has introduced challenges such as preserving security and privacy of sensitive data while maintaining the high quality of service. Thus, in this work, we will deliberately build up a RFID system by utilizing learning automata based multi agent intelligent system to greatly enhance and secure message transactions and to improve operational efficiency. The incorporation of these two advancements and technological developments will provide maximum benefit in terms of expertly and securely handle data in RFID scenario. In proposed work, learning automata inbuilt RFID tags or assumed players choose their optimal strategy via enlarging its own utility function to achieve long term benefit. This is possible if they transmit their utility securely to back end server and then correspondingly safely get new utility function from server to behave optimally in its environment. Hence, our proposed authentication protocol, expertly transfer utility from learning automata inbuilt tags to reader and then to server. Moreover, we verify the security and privacy of our proposed system by utilizing automatic formal prover Scyther tool.
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How this classification was reachedexpand
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 | 0.004 |
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 teacher head, 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".