Improved two-factor user authentication in wireless sensor networks
Bibliographic record
Abstract
Wireless sensor networks (WSNs) are considered due to the ubiquitous nature, ease of deployment, and wide range of possible applications. WSNs can be deployed in unattended environments, where a registered user can login to the network and access data collected by the linked sensors. Authenticating users in resource constrained environments is one of the major security concerns. Since sensor nodes have limited resources and computation power, it is desirable that the authentication protocol is simple and efficient. In 2009, M. L. Das proposed a two-factor authentication for WSNs, where a user has to prove possession of both, a password and a smart card. Since his scheme utilizes only cryptographic one-way hash function and exclusive-OR operation, it is well-suited for resource constrained environments. However, Khan and Algahathbar pointed out that Das's scheme has some flaws and is vulnerable to various attacks and proposed an alternative solution. In this paper, we show that both, Das's and Khan-Algahathbar's schemes have flaws and remain vulnerable to various attacks including stolen smart card attacks. To overcome the security weaknesses of both schemes, we propose an improved two-factor user authentication that is resilient to stolen smart card attacks as well as other common types of attacks. We provide security evaluation of the proposed protocols showing its robustness to various attacks and analyzed the scheme's performance to determine its efficiency. Compared to the previous schemes, it is proven more robust and provides better security.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".