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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. Machine Learning Model

Data Preprocessing and Feature Extraction from Opcodes

The raw opcodes extracted from smart contracts need to be converted into a format that machine learning algorithms can process. This involves several steps:

  • Tokenization: Opcodes are tokenized, breaking down the code into individual instructions or tokens.

  • Vectorization: The tokenized opcodes are then transformed into numerical vectors. Techniques like one-hot encoding or term frequency-inverse document frequency (TF-IDF) can be employed for this transformation.

  • Normalization: The feature vectors are normalized to ensure that the scale of the features does not bias the algorithms.

This preprocessing stage is crucial for effective machine learning analysis, as it directly impacts the model's ability to learn from the opcode data.

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Last updated 1 year ago

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