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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Abstract

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
Volume8
Pages (from-to)53972-53983
ISSN2169-3536
DOIs
Publication statusPublished - 1 Jan 2020

Keywords

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

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