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. RésuméActuellement, l'expérience utilisateur des réseaux sociaux en ligne (Online Social Networks) est difficile lorsqu'un utilisateur a à valider des identités d'autres pour se connecter.Compte tenu des attaques rampantes de vol d'identité sur OSN, il est parfois difficile de distinguer si la personne à connecter sur OSN est celle qui l'utilisateur connaît-il dans la vie réelle.Pour cette raison, la construction d'un système de validation d'identité est nécessaire pour protéger l'intérêt des utilisateurs ainsi que pour améliorer de l'expérience utilisateur.Dans cette thèse, nous présentons un système de validation d'identité -CredFinder pour OSN développé en plate-forme des dispositifs mobiles.Trois protocoles de validation sont conçus pour faire face à différents scénarios que d'utilisateurs peuvent-ils rencontrer.Concernant Facebook, nous proposons une implémentation basée sur Android prototype composée par trois sous-systèmes: application de dispositif mobile, serveur de validation et serveur d'application OSN.Les résultats et les analyses de l'implémentation démontrent que CredFinder est à la fois efficace et efficiente pour accomplir la validation d'identité.Au meilleur de nos connaissances, CredFinder est le premier système réel basé sur appareil mobile contre les attaques de vol l'identité sur OSN.La stratégie de validation dans notre système donne aux utilisateurs ordinaires le pouvoir de connecter sur leurs réseaux sociaux en ligne et hors ligne.Contents vi 6.1.4Location Service Attack . . . . . . . . . . . . . . . . . . . . . . . .6.2 Attacker With One Stolen Device . . . . . . . . . . . .
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".