Design and Implementation of a Controlled Ransomware Framework for Educational Purposes Using Flutter Cryptographic APIs on Desktop PCs and Android Devices
Bibliographic record
Abstract
This study focuses on the creation and implementation of ransomware for educational purposes that leverages Python’s native cryptographic APIs in a controlled environment. Additionally, an Android version of the framework is implemented using Flutter and Dart. For both versions, opensource cryptographic libraries are utilized. With this framework, researchers can systematically explore the functionalities of ransomware, including file encryption processes, cryptographic key management, and victim interaction dynamics. To ensure safe experimentation, multiple safeguards are incorporated, such as the ability to restrict the encryption process to a specific directory, providing the RSA private key for immediate decryption, and narrowing the scope of targetable files to a carefully curated list (.txt, .jpg, .csv, .doc). This paper draws inspiration from the infamous WannaCry ransomware and aims to simulate its behaviour on Android devices. By making the codebase opensource, it enables users to study, modify, and extend the program for pedagogical purposes and offers a hands-on tool that can be used to train the next generation of cybersecurity professionals.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".