Using a Privacy-Enhanced Authentication Process to Secure Internet of Things Infrastructures
Bibliographic record
Abstract
Abstract Over the last decade, technological advances in smart grids have permitted the modernization of legacy electricity networks. As Internet of Things(IoT)-based smart grids are becoming an efficient response to managing changing electric demand, the heterogeneous network of equipment required to make these Cyber-Physical Systems a reality poses some security threats. This paper proposes a novel mutual authentication and key agreement scheme to ensure communications security and protect users' privacy in smart grid applications. In the proposed scheme (named EPSG), an Elliptic Curve Cryptography (ECC) module and a Physical Unclonable Function (PUF) are used simultaneously to provide acceptable confidentiality and integrity levels. The security analysis demonstrates that the EPSG has a robust security posture regarding transferred messages on the communication channel and physical attacks. In addition, EPSG is resistant to modeling attacks as one of the main vulnerabilities of PUF modules. Furthermore, by implementing the EPSG on an Arduino UNO microcontroller, a comparative performance evaluation in terms of computational complexity, communication overhead, and power consumption demonstrates the efficiency of the proposed EPSG.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.000 | 0.002 |
| 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".