AI Integration

Machine Learning Models

1. Training Data: The models are trained on a diverse dataset of smart contracts, including both secure and vulnerable examples.

2. Feature Extraction: Key features such as function calls, variable declarations, and logical constructs are extracted from the contract code.

3. Model Types: Various models like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Transformer models are used to capture both syntactic and semantic patterns in the smart contracts.

4. Continuous Learning: The AI engine continuously learns from new data and feedback, improving its accuracy over time.

Vulnerability Detection

1. Common Vulnerabilities: The AI models are trained to detect well-known vulnerabilities such as reentrancy attacks, integer overflows, and underflows.

2. Anomaly Detection: The system can identify unusual patterns or anomalies that may indicate potential zero-day vulnerabilities.

3. Risk Assessment: Each detected issue is assigned a risk score, helping users prioritize their mitigation efforts.