Communities of Inquiry for Offenders: Learning Malware Development on Asynchronous Platforms
Bibliographic record
Abstract
Malware as a service (MaaS) has become a profitable profession, allowing individuals who are not technologically competent, and criminal organizations, to purchase such malicious software to conduct a variety of attacks. This has created space for those with the technological abilities to make a business off the malware that they write, and it is therefore important to understand where these developers are learning the skills needed. The current study was carried out to assess how malware developers use an encrypted messaging platform for knowledge acquisition, more specifically knowledge about malware development. This was carried out through a qualitative analysis of questions and answers posted within Telegram channels that are related to malware, and malware development and distribution. Further to this, latent class analysis was conducted to aid in determining whether there are subsets of individuals posting this information. A total of 467 user questions and 518 user responses were captured from eight channels. Results from this study revealed that posters are usually responsive to questions posed within these communities, with seven different response themes identified: Criticized question, offered answer or advice, offered help or service, probing for further information, provided resource, and unhelpful response. Therefore, while not many people are seeking Telegram channels to learn, when they do pose questions, respondents are likely to offer helpful advice to aid in their learning of malware development.
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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".