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A Blockchain-Enabled Secure Digital Twin Framework for Early Botnet Detection in IIoT Environment.

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This study introduces a Blockchain-enabled Digital Framework for early botnet detection in smart factories. It uses Digital Twins and Deep Learning to secure Industrial Internet of Things devices against cyberattacks.

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Area of Science:

  • Cybersecurity
  • Industrial Internet of Things (IIoT)
  • Smart Factory Environments

Background:

  • Resource constraints in IIoT enable brute-force attacks, leading to botnets and Distributed Denial of Service (DDoS) attacks.
  • Delayed botnet detection hinders control over malicious script spread and increases high-volume cyberattack risks.

Purpose of the Study:

  • To propose a secure Blockchain-enabled Digital Framework for early botnet detection in smart factories.
  • To enhance the security and resilience of Industrial Internet of Things environments against botnet formation.

Main Methods:

  • A Digital Twin (DT) inspects packet headers using Deep Learning for external connections.
  • Data synchronization between DT and Packet Auditor (PA) detects corrupt transmissions.
  • Smart Contracts authenticate DT and PA, preventing malicious node participation.
  • DT certificate revocation prevents botnet spread.

Main Results:

  • The framework ensures data integrity for botnet detection model training through DT-PA data synchronization.
  • Data privacy is maintained by inspecting only packet headers, avoiding data decryption.
  • Early detection of bot formation is achieved, mitigating cyberattack risks.

Conclusions:

  • The proposed Blockchain-enabled Digital Framework offers an effective solution for early botnet detection in smart factories.
  • The integration of Digital Twins, Deep Learning, and Blockchain enhances IIoT security and data integrity.
  • This approach successfully prevents botnet spread while preserving data privacy.