📘
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. References

Comprehensive list of academic and technical references supporting the research

  1. Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. https://bitcoin.org/bitcoin.pdf.

  2. Wood, G. (2014). Ethereum: A Secure Decentralised Generalised Transaction Ledger. Ethereum Project Yellow Paper.

  3. Zhou, Y., Kumar, N., Bakshi, S., & Mason, J. (2019). Machine Learning Techniques in Blockchain: A Survey. In Proceedings of the 2019 ACM Southeast Conference (ACM SE '19). Association for Computing Machinery.

  4. Alpaydin, E. (2020). Introduction to Machine Learning (4th ed.). MIT Press.

  5. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

  6. Miers, I., Garman, C., Green, M., & Rubin, A. D. (2013). Zerocoin: Anonymous Distributed E-Cash from Bitcoin. In 2013 IEEE Symposium on Security and Privacy.

  7. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques (4th ed.). Morgan Kaufmann.

  8. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. Springer.

  9. Antonopoulos, A. M. (2014). Mastering Bitcoin: Unlocking Digital Cryptocurrencies. O'Reilly Media.

  10. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

  11. Kelleher, J. D., & Tierney, B. (2018). Data Science. MIT Press.

  12. Lin, T., Rivest, R. L., Shamir, A., & Wagner, D. A. (1998). Efficient Collision Search Attacks on SHA-0. In Proceedings of the 17th Annual International Cryptology Conference on Advances in Cryptology.

  13. Swan, M. (2015). Blockchain: Blueprint for a New Economy. O'Reilly Media.

  14. Tama, B. A., & Rhee, K. H. (2017). A critical review of blockchain and its current applications. In 2017 International Conference on Electrical Engineering and Computer Science (ICECOS).

  15. Yaga, D., Mell, P., Roby, N., & Scarfone, K. (2018). Blockchain Technology Overview. National Institute of Standards and Technology Internal Report 8202.

PreviousFuture Directions for Research and System Enhancement

Last updated 1 year ago

📚