We The North Market : Fonctionnement et spécificités des échanges entre fraudeurs sur un marché illicite en ligne canadien
Bibliographic record
Abstract
Le nombre de marchés illicites permettant l'échange de produits frauduleux et de services illégaux a énormément augmenté au cours des dernières années.Certains de ces marchés clandestins sont opérés à partir Canada, où la fraude est une problématique bien présente.On constate que les cryptomarchés du Dark Web facilitent les activités criminelles des fraudeurs.La présente recherche de type exploratoire porte sur les échanges entre fraudeurs sur le marché illicite canadien nommé We The North.Une analyse de contenu des produits en vente et des discussions des forums de fraude du marché a été réalisée.Les résultats montrent que les caractéristiques des produits offerts, tels que le prix, la fréquence, le nombre de ventes ou le nombre de rétroactions varient selon leur type.Les types de produits frauduleux les plus fréquents sont les cartes de crédit (CVV), les dossiers de crédit et les identités volées.Les acheteurs se disent satisfaits des produits et les vendeurs peuvent obtenir des gains pouvant dépasser les 50 000$.L'analyse du forum montre que les fraudeurs utilisent les fils de discussion, entre autres pour partager leur avis sur les vendeurs et leurs produits, faire de la publicité, rechercher des produits et services, ainsi que s'entraider et s'échanger des conseils.Cette recherche permet alors de maximiser les connaissances sur un sujet peu étudié dans la littérature. A B S T R A C TThe number of illicit markets for exchanging fraudulent products and illegal services has increased drastically in recent years.Some of these underground marketplaces are operated from Canada, where fraud is a very present problem.It has been observed that Dark Web cryptomarkets facilitate the task of fraudsters in their criminal activities.This exploratory research focuses on exchanges between fraudsters on the Canadian illicit market called We The North.A content analysis of the products for sale and the discussions in the marketplace's fraud forums was conducted.The results show that the characteristics of the products offered on the market vary according to their type.The most common types of fraudulent products are credit cards (CVV), credit reports and stolen identities.Buyers report satisfaction with the products, and sellers can achieve gains that can exceed $50,000 through various sales strategies.Forum analysis shows that fraudsters use threads to share their opinions of sellers and their products, advertise, research products and services, help each other and share tips.This research maximizes knowledge on a topic that has yet to be studied in the literature.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".