Participatory detection of identity theft on mobile social platforms
Bibliographic record
Abstract
Popularity of smart devices has led to increasing use of social networking services for various purposes. Despite its benefits, the ubiquity of social network services introduces vulnerabilities to malicious behavior such as Sybil attacks or identity theft. As the 5G Era leads to the convergence of social, wireless, and mobile networks by enabling synergistic interplay between these networks, it is possible to take advantage of mobile edge computing in the detection of compromised social profiles in mobile and online social network platforms. In this paper, we propose a framework for participatory detection of identity theft on social networking platforms which would exploit the computing power of the user equipment. The proposed framework empowers the connections of a user in a social platform to cooperate on the verification of a social profile. Through a proof-of-concept study, we show that the proposed framework can detect anomalous behavior in the social profile by having each connection work on a different feature subset without semantic analysis. Our numerical results show that if the initial matching threshold in a decision tree is set properly, compromised accounts can be identified by the mobile platforms of the connections without undergoing heavy central processing.
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 | 0.009 |
| 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) | 0.001 | 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 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".