📘
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
Powered by GitBook
On this page
  1. Protection Oracle

Criteria and Metrics for Evaluating Opcode Maliciousness

  • Criteria: The model categorizes opcodes based on their likelihood of being malicious. Criteria could include patterns known to be associated with vulnerabilities or attacks, anomalous opcode sequences, or deviations from normal contract behavior.

  • Metrics: To assess the model's performance, metrics such as accuracy, precision, recall, and the F1 score are used. For instance, precision (the proportion of true positive results in all positive predictions) is crucial for minimizing false alarms, while recall (the proportion of true positive results in all actual positives) is important for ensuring no malicious opcode is missed.

Additionally, the area under the ROC curve (AUC-ROC) can be utilized to evaluate the model's ability to distinguish between malicious and benign opcodes across different threshold settings.

By meticulously following these steps and employing these criteria and metrics, the machine learning model can effectively analyze opcodes, aiding in the detection and prevention of potential security threats in blockchain contracts.

PreviousTraining, Validation, and Testing of the ModelNextRole and functionality of the protection oracle within the system

Last updated 1 year ago

🛡️