Convolutional Neural Network based algorithm for Early Warning Proactive System security in Software Defined Networks

Ahmed Janabi*, Triantafyllos Kanakis, Mark Johnson

*Corresponding author for this work

Research output: Contribution to JournalArticlepeer-review


Software-Defined Networking is an innovative architecture approach in the networking field. This technology allows networks to be centrally and intelligently managed by unified applications such as traffic classification and security management. Traditional networks’ static nature has a minimal capacity to meet organisations business requirements. Software-Defined Networks (SDNs) are the emerging architectures that address a range of networking challenges with new solutions. Nevertheless, these centralised and programmable techniques face various challenges and issues that require contemporary security solutions such as Intrusion Detection Systems. Recently, the majority of this type of security solution has been developed using Machine Learning techniques. Deep Learning algorithms have recently been used to provide more accuracy and efficiency. This paper presents a new detection approach based on Convolutional Neural Network (CNN). The experiments proved that the proposed model could be successfully implemented in a Software-Defined Network controller to detect various attacks with 100% accuracy, achieved a low degradation rate of 2.3% throughput and 1.8% latency when executed in a large-scale network.
Original languageEnglish
Article numberAccess-2021-41735
Pages (from-to)14301 - 14310
Number of pages10
JournalIEEE Access
Early online date31 Jan 2022
Publication statusPublished - 31 Jan 2022


  • Deep Learning-Early Warning Proactive System (DL-EWPS)
  • Convolutional Neural Network (CNN)
  • Software-Defined Networking (SDN)
  • Intrusion Detection System (IDS)
  • Deep Learning (DL)
  • RGB image
  • InSDN dataset


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