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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. Protection Oracle

Response mechanisms when malicious opcodes are detected

  1. Once the machine learning model identifies potentially malicious opcodes, the threat oracle initiates a predefined response protocol. This could include:

    • Alerting: Notifying system administrators or relevant stakeholders about the potential threat.

    • Quarantining: Temporarily isolating the suspicious contract to prevent its execution until further investigation.

    • Log Request: Triggering a deeper analysis or manual review of the suspicious contract recording a log.

    • Mitigation Suggestions: Allow for mitigation and tree-based analysis of the contract's structure and the calls it has made in cases where a malicious contract has already executed an attack (call graph)

These response mechanisms ensure that potential threats are promptly addressed, minimizing the risk and impact on the blockchain ecosystem.

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Last updated 1 year ago

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