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MFFLR-DDoS: An encrypted LR-DDoS attack detection method based on multi-granularity feature fusions in SDN.

Jin Wang1, Liping Wang1, Ruiqing Wang2

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

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This study introduces a new method to detect and mitigate low rate distributed denial of service (LR-DDoS) attacks, even on encrypted traffic. The MFFLR-DDoS approach effectively identifies and stops these stealthy cyber threats in real-time using SDN.

Keywords:
LR-DDoS attackSDNanomaly detectiondeep learningfeature fusionmalicious encrypted traffic

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Area of Science:

  • Cybersecurity
  • Network Security
  • Applied Computer Science

Background:

  • Low Rate Distributed Denial of Service (LR-DDoS) attacks exploit HTTP protocol vulnerabilities, causing prolonged server thread occupation and disrupting legitimate user access.
  • Traditional intrusion detection systems struggle to detect LR-DDoS attacks, particularly with encrypted HTTP traffic, due to their low request volume and slow speed.
  • Software Defined Networking (SDN) offers a flexible architecture for network management and security enforcement.

Purpose of the Study:

  • To propose and evaluate a novel method for detecting and mitigating encrypted LR-DDoS attacks within an SDN environment.
  • To enhance the detection accuracy and real-time response capabilities against sophisticated LR-DDoS threats.
  • To leverage multi-granularity feature fusion and deep learning for improved network security.

Main Methods:

  • Developed the Multi-Granularity Feature Fusion for LR-DDoS (MFFLR-DDoS) detection and mitigation method tailored for SDN.
  • Analyzed encrypted session flows by examining packet time sequences and session spatiality.
  • Utilized diverse deep learning techniques for feature extraction to identify abnormal traffic patterns.
  • Implemented real-time defense mechanisms through SDN controller-issued flow rules.

Main Results:

  • The MFFLR-DDoS model demonstrated a significantly higher detection rate compared to existing advanced methods.
  • The proposed method successfully mitigated LR-DDoS attack traffic in real-time.
  • Effective feature extraction from multi-granularity data improved the accuracy of abnormal traffic detection.

Conclusions:

  • The MFFLR-DDoS method provides an effective solution for detecting and mitigating stealthy LR-DDoS attacks on encrypted traffic within SDN networks.
  • The fusion of multi-granularity features and deep learning significantly enhances detection capabilities.
  • SDN architecture enables efficient, real-time online defense against LR-DDoS attacks.