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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. System Architecture

Architecture components

PreviousOverview of the proposed system and its significance in the blockchain domainNextWorkflow and Interaction Between the Threat Oracle and the Machine Learning Model

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  • Threat Oracle: This component acts as the central intelligence hub of the system. It is responsible for gathering data from blockchain contracts, initiating the analysis process, and interpreting the results from the machine learning model. The threat oracle serves as the intermediary between raw contract data and actionable insights.

  • Machine Learning Model: At the core of the system is a machine learning model designed to analyze contract opcodes. This model is trained on a dataset of known benign and malicious opcodes, learning to identify patterns and indicators of potentially malicious behavior in new contracts.

  • Contract Analysis Module: This module is responsible for extracting opcodes from smart contracts and preparing them for analysis. It acts as the preprocessing step where data is cleaned, normalized, and converted into a format suitable for the machine learning model.

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Figure 2 - Neurablock architecture