Orchestrating SDN Control Plane towards Enhanced IoT Security

Tooba Hasan, Adnan Akhunzada, Athanasios Giannetsos, Jahanzaib Malik

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Abstract

The Internet of Things (IoT) is rapidly evolving, while introducing several new challenges regarding security, resilience and operational assurance. In the face of an increasing attack landscape, it is necessary to cater for the provision of efficient mechanisms to collectively detect sophisticated malware resulting in undesirable (run-time) device and network modifications. This is not an easy task considering the dynamic and heterogeneous nature of IoT environments; i.e., different operating systems, varied connected networks and a wide gamut of underlying protocols and devices. Malicious IoT nodes or gateways can potentially lead to the compromise of the whole IoT network infrastructure. On the other hand, the SDN control plane has the capability to be orchestrated towards providing enhanced security services to all layers of the IoT networking stack. In this paper, we propose an SDN-enabled control plane based orchestration that leverages emerging Long Short-Term Memory (LSTM) classification models; a Deep Learning (DL) based architecture to combat malicious IoT nodes. It is a first step towards a new line of security mechanisms that enables the provision of scalable AI-based intrusion detection focusing on the operational assurance of only those specific, critical infrastructure components,thus, allowing for a much more efficient security solution. The proposed mechanism has been evaluated with current state of the art datasets (i.e., N BaIoT 2018) using standard performance evaluation metrics. Our preliminary results show an outstanding detection accuracy ( i.e., 99.9%) which significantly outperforms state-of-the-art approaches. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them, so that security does not hinder the deployment of intelligent IoT-based computing systems.
Original languageEnglish
Title of host publicationProceedings of 2020 IEEE Conference on Network Softwarization
PublisherIEEE
Publication date2020
Pages457-64
ISBN (Print)9781728156842
DOIs
Publication statusPublished - 2020
Event6th IEEE International Conference on Network SoftwarizationIEEE International Conference on Network Softwarization - Virtual event
Duration: 29 Jun 20203 Jul 2020
https://netsoft2020.ieee-netsoft.org/

Conference

Conference6th IEEE International Conference on Network SoftwarizationIEEE International Conference on Network Softwarization
LocationVirtual event
Period29/06/202003/07/2020
Internet address

Keywords

  • Deep learning
  • IoT botnet
  • LSTM
  • Network security
  • Software Defined Network

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