📘
Whitepaper
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  • 👋Introduction
    • Enhancing Blockchain Contract Security: A Machine Learning Approach to Opcode Analysis by Neurablock
    • The importance of opcode analysis in contract security
    • Overview of the proposed system and its significance in the blockchain domain
  • ⚙️System Architecture
    • Architecture components
    • Workflow and Interaction Between the Threat Oracle and the Machine Learning Model
    • Contract protection oracle integration
  • 🤖Machine Learning Model
    • Description of the Machine Learning Algorithms Used for Opcode Testing Analysis
    • Data Preprocessing and Feature Extraction from Opcodes
    • Training, Validation, and Testing of the Model
  • 🛡️Protection Oracle
    • Criteria and Metrics for Evaluating Opcode Maliciousness
    • Role and functionality of the protection oracle within the system
    • Integration of the protection oracle with the machine learning model
    • Response mechanisms when malicious opcodes are detected
    • Webapp protection/monitoring
  • 🔢Mathematical Formulations
    • Formulas and Algorithms Used in Opcode Analysis and Threat Assessment
      • Mathematical Rationale Behind the Machine Learning Algorithms Employed
    • Theoretical Underpinnings of the System's Decision-Making Process
    • Presentation of the System's Performance in Detecting Malicious Opcodes
  • 🛠️Tools
    • Pioneering Cybersecurity Tools Powered by AI for Web 3.0
  • ✅Results & conclusions
    • Summary of the Key Findings and System Capabilities
    • Future Directions for Research and System Enhancement
  • 📚References
    • Comprehensive list of academic and technical references supporting the research
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  1. System Architecture

Workflow and Interaction Between the Threat Oracle and the Machine Learning Model

  • The workflow begins with the threat oracle, which monitors the blockchain for new or modified contracts. Upon detecting a contract, the oracle extracts its details and passes them to the contract analysis module.

  • The contract analysis module processes the contract, extracting and preparing the opcodes for analysis. This processed data is then fed into the machine learning model.

  • The machine learning model analyzes the opcodes, assessing them for patterns or indicators that suggest malicious intent. The results of this analysis are then passed back to the threat oracle.

  • Based on the analysis results, the threat oracle makes informed decisions on whether the contract poses a security risk and takes appropriate actions, such as blocking the interaction with the destination contract.

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Last updated 1 year ago

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