Technical Details

Machine Learning Models

DecentraScan utilizes a combination of machine learning models tailored to specific aspects of smart contract auditing:

  • Neural Networks: For deep analysis of contract code, identifying complex vulnerabilities.

  • Decision Trees: For rule-based detection of common issues.

  • Support Vector Machines: For classifying contracts based on risk levels.

Data Collection and Training

The AI models are trained on a diverse dataset, including:

  • Historical Smart Contracts: A comprehensive repository of past contracts, annotated with known vulnerabilities.

  • Security Reports: Detailed analyses from security experts, providing insights into common issues and best practices.

  • Blockchain Data: Real-time data from the Ethereum blockchain, ensuring models are up-to-date with the latest contract developments.

Continuous Learning

DecentraScan implements a continuous learning framework:

  • Feedback Mechanism: Users can report false positives/negatives, allowing the system to learn from its mistakes.

  • Automated Updates: Regular updates to the AI models based on new data and evolving threat landscapes.

  • Community Contributions: Open-source contributions from the community to enhance the dataset and improve model accuracy.

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