Efficient Detection of Link-Flooding Attacks with Deep Learning

Chih-Hsiang Hsieh, Wei-Kuan Wang, Chen-Xun Wang, Shi-Chun Tsai*, Yi-Bing Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The DDoS attack is one of the most notorious attacks, and the severe impact of the DDoS attack on GitHub in 2018 raises the importance of designing effective defense methods for detecting this type of attack. Unlike the traditional network architecture that takes too long to cope with DDoS attacks, we focus on link-flooding attacks that do not directly attack the target. An effective defense mechanism is crucial since as long as a link-flooding attack is undetected, it will cause problems over the Internet. With the flexibility of software-defined networking, we design a novel framework and implement our ideas with a deep learning approach to improve the performance of the previous work. Through rerouting techniques and monitoring network traffic, our system can detect a malicious attack from the adversary. A CNN architecture is combined to assist in finding an appropriate rerouting path that can shorten the reaction time for detecting DDoS attacks. Therefore, the proposed method can efficiently distinguish the difference between benign traffic and malicious traffic and prevent attackers from carrying out link-flooding attacks through bots.
Original languageAmerican English
Article number12514
Issue number22
StatePublished - Nov 2021


  • Deep learning (DL)
  • Distributed denial of service (DDoS) attack
  • Link-flooding attack (LFA)
  • Software defined networking (SDN)


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