Risky Business? Investigating the Security Practices of Vendors on an Online Anonymous Market using Ground-Truth Data
Bibliographic record
Abstract
Cybercriminal entrepreneurs on online anonymous markets rely on security mechanisms to thwart investigators in at- tributing their illicit activities. Earlier work indicates that – despite the high-risk criminal context – cybercriminals may turn to poor security practices due to competing business incentives. This claim has not yet been supported through empirical, quantitative analysis on ground-truth data. In this paper, we investigate the security practices on Hansa Mar- ket (2015-2017) and measure the prevalence of poor security practices across the vendor population (n = 1, 733). We create ‘vendor types’ based on latent profile analysis, clustering vendors that are similar regarding their experience, activity on other markets, and the amount of physical and dig- ital items sold. We then analyze how these types of vendors differ in their security practices. To that end, we capture their password strength and password uniqueness, 2FA usage, PGP adoption and key strength, PGP-key reuse and the traceability of their cash-out. We find that insecure practices are prevalent across all types of vendors. Yet, between them large differ- ences exist. Rather counter-intuitively, Hansa Market vendors that sell digital items – like stolen credit cards or malware – resort to insecure practices more often than vendors selling drugs. We discuss possible explanations, including that ven- dors of illicit digital items may perceive their risk to be lower than vendors of illicit physical items.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".