Is Your PIN Safe Against Advanced Human-Centric Shoulder Surfing?
Bibliographic record
Abstract
Personal Identification Numbers (PINs) are a widely used authentication method, especially in systems with limited user input interfaces. Although numerous studies have investigated the vulnerabilities of PINs against various cyber threats, some attacks, such as basic shoulder surfing, are often mitigated by enhancing the complexity of the user-interface. This paper challenges that conventional approach by introducing an attack strategy that builds upon three basic classifiers−Decision Tree, Random Forest, and Naive Bayes. Through the examination of both four-digit and six-digit PINs, the findings reveal that even with only partial knowledge of a captured PIN sequence−due to the cognitive limitations of human adversaries−it is possible to predict the remaining digits of the PIN with a significant success rate. In some cases, this rate exceeds 50%, which is considerably higher than the 10% success rate expected from random guessing. Despite the diminished effectiveness of the proposed attack model for six-digit PINs, the results of this preliminary research are compelling enough to question the assumed ineffectiveness of Human-Centric Shoulder Surfing (HCSS).
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".