Bibliographic record
Abstract
In South Korea, voice phishing has been proliferating with the advent of voice phishing apps: the number of annual victims had risen to 34,527 in 2020, representing financial losses of approximately 598 million USD. However, the voice phishing functionalities that these abusive apps implement are largely understudied. To this end, we analyze 1,017 voice phishing apps and reveal new phishing functionalities: outgoing call redirection, call screen overlay, and fake call voice. We find that call redirection that changes the intended recipients of victims' outgoing calls plays a critical role in facilitating voice phishing; our user study shows that 87% of the participants did not notice that their intended recipients were changed when call redirection occurred. We further investigate implementations of these fatal functionalities to distinguish their malicious behaviors from their corresponding behaviors in benign apps. We then propose HearMeOut, an Android system-level service that detects phishing behaviors that phishing apps conduct in runtime and blocks the detected behaviors. HearMeOut achieves high accuracy with no false positives or negatives in classifying phishing behaviors while exhibiting an unnoticeable latency of 0.36 ms on average. Our user study demonstrates that HearMeOut is able to prevent 100% of participants from being phished by providing active warnings. Our work facilitates a better understanding of recent voice phishing and proposes practical mitigation with recommendations for Android system changes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".