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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. Introduction

Overview of the proposed system and its significance in the blockchain domain

NeurablockThe proposed system introduces a novel approach to enhancing blockchain contract security. By focusing on a contract-to-contract analysis, it ensures a granular level of scrutiny, assessing each contract's operations independently without affecting the broader EOA wallet operations. This approach is critical in a landscape where traditional security measures may fall short in addressing the unique challenges posed by smart contracts.

The system's integration of a 'threat oracle' with a machine learning model represents a significant advancement in the domain. This combination allows for dynamic and intelligent analysis of opcodes, enabling the system to adapt and respond to emerging threats. By leveraging machine learning, the system can evolve, learning from new patterns of vulnerabilities and enhancing its detection capabilities over time.

The significance of this system in the blockchain domain cannot be overstated. It offers a proactive and intelligent solution to contract security, addressing a critical need in the blockchain ecosystem. By enhancing the security of smart contracts, the system not only protects individual users and organizations but also bolsters the overall trust and stability of blockchain technology.

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Last updated 1 year ago

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