A Deep CNN Ensemble Framework for Efficient DDoS Attack Detection in Software Defined Networks

Shahzeb Haider, Adnan Akhunzada*, Iqra Mustafa, Tanil Bharat Patel, Amanda Fernandez, Kim Kwang Raymond Choo, Javed Iqbal

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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As novel technologies continue to reshape the digital era, cyberattacks are also increasingly becoming more commonplace and sophisticated. Distributed denial of service (DDoS) attacks are, perhaps, the most prevalent and exponentially-growing attack, targeting the varied and emerging computational network infrastructures across the globe. This necessitates the design of an efficient and early detection of large-scale sophisticated DDoS attacks. Software defined networks (SDN) point to a promising solution, as a network paradigm which decouples the centralized control intelligence from the forwarding logic. In this work, a deep convolutional neural network (CNN) ensemble framework for efficient DDoS attack detection in SDNs is proposed. The proposed framework is evaluated on a current state-of-the-art Flow-based dataset under established benchmarks. Improved accuracy is demonstrated against existing related detection approaches.

Original languageEnglish
Article number9016053
JournalIEEE Access
Pages (from-to)53972-53983
Publication statusPublished - 1 Jan 2020


  • Anomaly detection
  • Deep convolutional neural network (CNN)
  • Deep learning
  • Distributed denial of service (DDoS)
  • Software defined network (SDN)


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