A hybrid firefly-based attribute selection and split-point mechanism for securing software-defined industrial Internet of Things

Abstract

Cybersecurity has increased on software-defined networks (SDNs) since its inception in the early 2010s. That is due to the SDN's centralized nature in controlling the network, human-centered generative methods, and transferring data in the internet of behavior. The widespread usage of Internet of Things (IoT) networks, integrating SDNs with IoT, the intelligent industrial revolution, and the inclusion of the human-centric industrial revolution in the infrastructure of major industrial fields all were reasons to focus on creating effective attack detection systems intrusion detection system (IDS) for these industrial IoT (IIoT) networks. In this paper, we present an IDS we call SPAARC_DDSDN_IIoT. It is a four-layer industrial IoT architecture weaponized by security devices at each layer and uses the SDN's centralized approach. First is an application layer-based prediction approach called SPAARC. SPAARC is a split-point algorithm combined with an attribute-reduced classifier (SPAARC). It is applied to the proposed IDS-based SDN approach in the IIoT. SPAARC is a decision tree algorithm, and intrusion detection will be performed on the leaves of the resulting tree. Two datasets were used for the experiment: the DDoS_SDN and the XIIoT_ID. The firefly algorithm, as a swarm intelligence based on the behavior of fireflies, was used for feature selection. SPAARC achieved a notable accuracy of 99.9962% and 99.991%, surpassing all the other machine learning algorithms tested. SPAARC also achieved a mean absolute error of 0 and 0.008, a root mean squared error of 0.0062 and 0.0016, and a perfect score on both datasets for the remaining metrics.