287Chapter 12 Two-Factor Authentication (2FA) and Multi-factor Authentication (MFA) Solutions for Secure Mobile Data Communication
Bibliographic record
Abstract
The increasing reliance on mobile platforms has increased the demand for robust, scalable, and effective data security mechanisms. This paper introduces an overall framework that combines two-factor authentication (2FA), multi-factor authentication, and ciphertext-policy attribute-based proxy re-encryption (CAPRE) using elliptic curve cryptography (ECC) to secure mobile data transmission. The system under design counteracts the limitations of traditional authentication schemes, such as vulnerability to impersonation, key compromise, and inefficient key management, by utilizing a multi-dimensional authentication model that combines knowledge-based, possession-based, and biometric authentication techniques. ECC is utilized for efficient cryptographic operations and ensuring security, making the framework suitable for low-resource environments. CAPRE enables careful access control and secure delegation of decryption authority without exposing plaintext data. Experimental tests demonstrate the framework’s efficacy in curbing encryption, decryption, re-encryption times, and energy consumption without compromising good resistance to phishing, brute force, and man-in-the-middle attacks. The results confirm that the proposed approach greatly enhances data security, authentication integrity, and computational efficiency, rendering it the best for current mobile contexts.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.103 | 0.054 |
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".