> For the complete documentation index, see [llms.txt](https://decentrascan.gitbook.io/decentrascan/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://decentrascan.gitbook.io/decentrascan/ai-integration.md).

# 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.