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The industrial Internet of Things (IIoT) involves the integration of Internetof Things (IoT) technologies into industrial settings. However, given the highsensitivity of the industry to the security of industrial control systemnetworks and IIoT, the use of software-defined networking (SDN) technology canprovide improved security and automation of communication processes. Despitethis, the architecture of SDN can give rise to various security threats.Therefore, it is of paramount importance to consider the impact of thesethreats on SDN-based IIoT environments. Unlike previous research, which focusedon security in IIoT and SDN architectures separately, we propose an integratedmethod including two components that work together seamlessly for betterdetecting and preventing security threats associated with SDN-based IIoTarchitectures. The two components consist in a convolutional neuralnetwork-based Intrusion Detection System (IDS) implemented as an SDNapplication and a Blockchain-based system (BS) to empower application layer andnetwork layer security, respectively. A significant advantage of the proposedmethod lies in jointly minimizing the impact of attacks such as commandinjection and rule injection on SDN-based IIoT architecture layers. Theproposed IDS exhibits superior classification accuracy in both binary andmulticlass categories.

Blockchain and Deep Learning-Based IDS for Securing SDN-Enabled Industrial IoT Environments