A Machine Learning Approach of Predicting Ransomware Addresses in BlockChain Networks
Bibliographic record
Abstract
Ransomware attacks, particularly those employing cryptocurrencies such as Bitcoin, pose a significant risk to worldwide cybersecurity. The decentralized structure of these networks, coupled with the anonymity they afford, complicates the tracking and cessation of criminal operations. This paper presents an extensive analysis focused on improving ransomware detection utilizing advanced machine learning and deep learning approaches on multiclass ransomware classification. Our investigation employed three principal models: Bidirectional Long Short-Term Memory (BiLSTM), Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM). To address inherent class imbalances and enhance model efficacy, we incorporated advanced techniques, including Topological Data Analysis (TDA), Focal Loss, and Synthetic Data Generation. Topological Data Analysis (TDA) revealed complex data patterns that conventional approaches may overlook, while Focal Loss was integrated to emphasize difficult instances and address class imbalance. Moreover, Generative Adversarial Networks (GANs) produced synthetic data, improving dataset equilibrium and fortifying model training. In multiclass classification, the BiLSTM model emerged as the superior performer, attaining an accuracy of 85.54% and exceeding both MLP and SVM. This underscores the versatility and efficacy of BiLSTM across multiple prediction categories. Our research highlights the essential integration of TDA, GAN, and focal loss to improve feature representation and model resilience in ransomware detection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".