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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. Mathematical Formulations

Formulas and Algorithms Used in Opcode Analysis and Threat Assessment

  • Feature Extraction: Utilize mathematical formulations for converting opcodes into numerical features. For example, one-hot encoding can be represented as a vector transformation where each opcode is transformed into a binary vector with a '1' in the position corresponding to the opcode and '0's elsewhere.

  • Sequence Analysis: For sequential data like opcodes, employ algorithms such as Hidden Markov Models (HMM) or Recurrent Neural Networks (RNNs). The HMM, for instance, uses transition probabilities (a_ij) between states (opcodes) and emission probabilities (b_j(k)) to model the sequence.

  • Threat Assessment: Implement classification algorithms, where the decision function for a simple linear classifier could be y=wTx+by=wTx+by=wTx+b, where www is the weight vector, xxx is the feature vector, and bbb is the bias. The sign of yyy determines the classification (malicious or benign).

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Last updated 1 year ago

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