Secret sharing in online communities: A comparative analysis of offender and non-offender password creation strategies
Bibliographic record
Abstract
Even though several authentication methods exist, passwords remain the most common type of authentication. Researchers have demonstrated the influence of a person’s environment and exposure to the Internet on their online security behavior (Bosnjak & Brumen, 2016; He et al., 2021; Juozapavičius et al., 2022). Those studies suggest that social identity seems to play a role in password choice. The objective of this study was to determine if the criminal nature of a network influences password-creation strategies. To achieve this, we utilized two databases with a substantial number of actual passwords (1,485,095) that had been leaked to the Internet. One database was sourced from a non-delinquent social network, while the other was from a hacker forum. We employed logistic regression to reveal the characteristics associated with each group, ensuring a comprehensive analysis of different types of password strategies and the similarity between actors of the same network. Results show that users of the same network have passwords with characteristics that are similar to each other. Individuals with the same social interests seem more likely to use the same password-creation strategies. From a network analysis perspective, the results show that similar individuals (sharing the same interests) are similar in other aspects (password creation strategies). These findings offer valuable insights into the diverse landscape of password varieties and user behaviors, contributing to a more comprehensive understanding of internet user networks.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.003 | 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".