Overview
This invention presents a Transformer-based deep learning model for detecting abnormal transactions in blockchain-based cryptocurrencies. By capturing expressive behavioral patterns and sequential dependencies, the model achieves higher accuracy compared to traditional methods.
Key Components
1. Methodology
Step 1: Data Sampling & Preprocessing
- Collect normal and abnormal transaction data.
- Serialize each user’s transactions chronologically to create labeled sequences (normal vs. abnormal).
Key Attributes Used:
- Value: Transferred currency amount.
- Gas: Includes
limit(computation cap),used(actual computation), andprice(cost per unit). - Addresses: Sender/receiver identifiers.
Step 2: Model Training
- Encode sequences into vectors using Word2Vec and Positional Encoding.
- Train a Transformer-based model to classify transactions.
- Optimization: Test multiple sampling ratios (e.g., 100:1 to 10,000:1 for normal:abnormal) to handle data imbalance.
2. Technical Advantages
✅ Transformer Architecture:
- Superior parallel processing for large-scale transaction data.
- Long-range dependency capture (vs. RNN/LSTM limitations).
✅ Feature Selection:
- Focuses on high-impact attributes (e.g.,
value,gas) while discarding noisy ones (e.g., hash).
✅ Preprocessing Innovation:
- Chronological sequencing enhances temporal pattern recognition.
Implementation Workflow
Phase 1: Data Collection
- Extract transaction timelines via block indices (since blockchain lacks explicit timestamps).
- Aggregate per-user transactions into structured sequences.
Phase 2: Model Deployment
- Training: Use supervised learning with cross-entropy loss.
Evaluation: Measure accuracy via:
\text{Accuracy} = \frac{\text{Correct Predictions}}{\text{Total Test Samples}}
👉 Explore Transformer Applications in Crypto
FAQs
Q1: Why use Transformers over RNNs?
A: Transformers handle long sequences more efficiently and avoid gradient issues common in RNNs.
Q2: How is data imbalance addressed?
A: Multiple sampling ratios (e.g., 100:1) are tested to optimize model performance.
Q3: Which transaction attributes matter most?
A: Value, gas fields, and addresses are critical; hash data is ignored.
Conclusion
This method leverages deep learning to improve cryptocurrency transaction monitoring, offering scalability and precision for fraud detection.
👉 Learn More About Blockchain Security
### Keywords:
1. Cryptocurrency
2. Anomaly Detection
3. Transformer Model
4. Blockchain
5. Gas Attributes
6. Sequential Data
7. Fraud Prevention