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SDN-Defend: A Lightweight Online Attack Detection and Mitigation System for DDoS Attacks in SDN.

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  • 1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.

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This study introduces a Software Defined Networking (SDN) defense system to combat distributed denial of service (DDoS) attacks. The system uses deep learning for real-time detection and IP traceback for mitigation, ensuring network availability.

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Area of Science:

  • Computer Science
  • Network Security
  • Cybersecurity

Background:

  • Software Defined Networking (SDN) offers centralized control, increasing vulnerability to distributed denial of service (DDoS) attacks.
  • DDoS attacks in SDN can exhaust controller/switch memory and network resources, disrupting normal user access.

Purpose of the Study:

  • To design and implement an online defense system for detecting and mitigating DDoS attacks in SDN environments.
  • To enhance the security and resilience of SDN infrastructure against sophisticated cyber threats.

Main Methods:

  • Developed a two-module defense system: anomaly detection and mitigation.
  • Employed a hybrid deep learning model (Convolutional Neural Network-Extreme Learning Machine) for traffic anomaly detection.
  • Utilized IP traceback for attacker identification and flow rule commands for traffic filtering.

Main Results:

  • The system accurately identifies DDoS attack traffic in real-time.
  • The implemented mitigation module effectively filters malicious traffic.
  • Experimental evaluation confirms the system's capability to detect and mitigate attacks.

Conclusions:

  • The proposed SDN defense system provides an effective solution for real-time DDoS attack detection and mitigation.
  • The hybrid CNN-ELM approach offers a lightweight yet powerful method for anomaly detection in SDN.
  • The system enhances SDN security, ensuring network stability and service availability.