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Area of Science:

  • Computer Science
  • Network Security
  • Cybersecurity

Background:

  • IPv6 extension headers offer flexibility but introduce security vulnerabilities like firewall evasion.
  • Current threat detection methods lack universality, speed, and comprehensive detection capabilities.
  • Existing solutions struggle with complex threats such as header disorder and repetition.

Purpose of the Study:

  • To propose an adaptive detection model for IPv6 extension header threats (ADM-DDA6).
  • To enhance the speed and accuracy of detecting diverse IPv6 extension header security threats.
  • To develop a model that minimizes performance overhead on network infrastructure.

Main Methods:

  • Designed standard rule sets for common IPv6 extension headers, detecting 70 threats with 20 rules.
  • Developed a method to parse headers, match rules, establish transitions, and determine packet threat status.
  • Implemented an adaptive rule matching technique that dynamically selects rule sets based on header types.

Main Results:

  • Successfully detected 70 threat types using a concise rule set.
  • The adaptive model demonstrated superior detection speed, outperforming Suricata and Snort.
  • ADM-DDA6 showed minimal CPU overhead (0.7%) and no significant memory increase, even with increased threats.

Conclusions:

  • ADM-DDA6 effectively detects common and complex IPv6 extension header threats.
  • The adaptive approach significantly enhances detection speed and efficiency.
  • The model provides a scalable and performant solution for securing IPv6 networks.