A novel key management protocol for RFID systems
Bibliographic record
Abstract
The increasing demand to deploy efficient ways of identification has made Radio frequency Identification (RFID) technology ubiquitous. Due to the wireless nature of communication between the reader and the tag, this technology imposes major security and privacy threats. Massive work to design a powerful authentication protocol has been put to overcome various threats against privacy and security of the system. However, certain constraints on RFID tags such as limited computation capabilities, memory size and communication cost, has made most approaches fail to conduct fully secured RFID system. In this paper, we present a novel cryptographic scheme, Hacker Proof Authentication Protocol (HPAP), that allows mutual authentication between the reader and the tag as well as secure tags' information. We prove our protocol achieves full security by deploying tag static identifier, updated timestamp, a one way hash function and encryption keys with semi randomized nature as they are updated using Linear Feedback Shift Register (LFSR). Simulation using C#.NET shows that the protocol is secure against various attacks. Comparison against various existing RFID authentication protocols prove that our protocol maintain less storage, computation load and low-cost.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".