  The industrial Internet of Things (IIoT) involves the integration of Internet
of Things (IoT) technologies into industrial settings. However, given the high
sensitivity of the industry to the security of industrial control system
networks and IIoT, the use of software-defined networking (SDN) technology can
provide improved security and automation of communication processes. Despite
this, the architecture of SDN can give rise to various security threats.
Therefore, it is of paramount importance to consider the impact of these
threats on SDN-based IIoT environments. Unlike previous research, which focused
on security in IIoT and SDN architectures separately, we propose an integrated
method including two components that work together seamlessly for better
detecting and preventing security threats associated with SDN-based IIoT
architectures. The two components consist in a convolutional neural
network-based Intrusion Detection System (IDS) implemented as an SDN
application and a Blockchain-based system (BS) to empower application layer and
network layer security, respectively. A significant advantage of the proposed
method lies in jointly minimizing the impact of attacks such as command
injection and rule injection on SDN-based IIoT architecture layers. The
proposed IDS exhibits superior classification accuracy in both binary and
multiclass categories.
