Network Security Evaluation Scheme for WSN in Cyber-physical Systems
Bibliographic record
Abstract
Cyber-Physical Systems (CPSs) and Internet of Things (IoT) have seen burgeoning growth in every sphere of life. With this growth, researchers now face new challenges in sensor network security. Most of the research in this area only deals with vulnerabilities, attacks and countermeasures. However, considering security of WSN as a comprehensive unit in the practical deployment of CPS is still missing. System engineers need to assess the performance of a WSN against attacks and failures so that they may design reliable and stable networks. In this paper, we propose a novel multi-level Network Security Evaluation Scheme (NSES) to represent different security levels. The main objective of this evaluation scheme is to help system engineers and security experts to be able to assess the security needs of their networks and maintain the required protection level of the network at early design phases. Through several case studies, we have demonstrated the application of this scheme to evaluate and assess the security in different scenarios. These case studies also help in endorsing the usability of the proposed scheme across different application domains.
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 | 0.000 | 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".