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Whitepaper
Twitter
  • 👋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

Presentation of the System's Performance in Detecting Malicious Opcodes

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Last updated 1 year ago

Throughout the year 2023, our system was rigorously tested in real-world conditions to evaluate its effectiveness in detecting malicious opcodes within blockchain contracts. The results are promising and showcase the system's potential in enhancing blockchain security.

During this period, a total of 131 attacks were recorded in the blockchain ecosystem, as reported by Neura_Block's tweet (). Our system successfully identified 110 of these attacks, demonstrating a detection rate of 83.97%. This high success rate underscores the system's proficiency in analyzing and identifying potentially malicious activities within blockchain contracts.

The detection of 110 out of 131 total attacks represents a significant achievement for our system, indicating its robustness and effectiveness in real-time threat identification. These results highlight the system's potential to serve as a critical tool in the ongoing effort to secure blockchain environments against malicious exploits.

By continuously refining our algorithms and enhancing our data analysis techniques, we aim to further improve the system's detection capabilities. The ongoing learning process, coupled with an increasing understanding of threat patterns, positions our system as a formidable defense mechanism in the evolving landscape of blockchain security.

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https://twitter.com/Neura_Block/status/1738123447983607845