MDA: message digest-based authentication for mobile cloud computing
Bibliographic record
Abstract
The emerging area of mobile cloud computing will influence the future of varied applications, such as electronic commerce and health informatics. It is expected to rise in popularity over other models in cloud computing. This is facilitated by its simplicity, accessibility and ease of use. With mobile cloud computing, resource-constrained mobile devices could capitalize on the computation/storage resources of cloud servers via communication networks. Despite the advantage of this innovative computing model, mobile devices in mobile cloud computing are open to more security risks because they often have to access cloud servers through untrusted networks from different locations. Therefore, security is a critical problem to be tackled in mobile cloud computing. One of the most important aspects of mobile cloud computing security is to establish authenticated communication sessions between mobile devices and cloud servers. In this paper, we present a novel authentication scheme, Message Digest-based Authentication (MDA). Technically, MDA strategically incorporates hashing, in addition to traditional user ID and passwords, to achieve mutual authentication. The effectiveness of MDA is validated with Scyther, a widely-used security protocol analyzer. Our experimental results indicate that MDA is capable of withstanding a variety of different security attacks, such as man-in-the-middle, replay attacks, etc.
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 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 | 0.004 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".