A mobile device based identity validation system for online social networks
Bibliographic record
Abstract
Currently, online social networks (OSNs) do not provide validation mechanisms to verify the identity of a user who is seeking linkage with another user. This shortfall is exploited by attackers to infiltrate other people's social circles to gain access to personal data. Therefore, building an identity validation system is necessary for protecting the user interest as well as enhancing the user experience.In this thesis I present an identity validation system---CredFinder for OSNs using commodity mobile devices. Three validation protocols are designed under different scenarios people may encounter. Targeted on Facebook, we propose an Android based prototypical implementation including three subsystems, the mobile device application, the validation server and the OSN application server. The implementation results demonstrate that CredFinder is capable of performing identity validation. To the best of our knowledge, CredFinder is the first mobile device based practical system against social network identity theft attacks. The validation strategy in our system gives users the power to connect their online and offline social networks together.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".