RFID Clone Detection in Supply Chain Using Modified Count-Min and BASE Protocol
Bibliographic record
Abstract
Radio Frequency Identification (RFID) clone in supply chain system causes money, and reputation loss.Existing algorithm detects the clone in RFID tag, never detect clone based on the distance between reader and tag.Modified BASE (MB) protocol is modified version of Binary Search (BASE) protocol.Modification in MB is performed based on logical operations and calculations, identify duplicate tags and duplicate reading.Modified Count-Min (MCM) protocol enhances clone detection by empowering reader device and recognizes cloned RFID tags using a local database, ensures tag-based verification in local and online data.Integration of this local database aids in clone detection and streamlines computational complexity through effective encryption and decryption processes, strengthens security and reliability of supply chain.In this paper, RFID clone detection technique using MB and MCM methods solve the problem of countering distance-fraud attacks after ensuring proximity between tag and reader, ensures security in wireless authentication.Proposed MB and MCM authentication protocol avoid cloning through data comparison in embedded based SQL database in reader.From experimental analysis, proposed methods detect clone in supply chain system with 0.2 seconds, detection accuracy is 90% for MCM and MB of 92%.The computation time of MCM is 0.5sec, 7sec for existing algorithm.
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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 | 0.000 |
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 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".