A systematic review of metaheuristic based feature selection strategies for cyber-attack detection in the IIoT

The Industrial Internet of Things (IIoT) has been spreading across all fields of applicable environments, one of the most crucial ones being the industrial environment. IIoT is the integration of the internet within various industrial fields such as smart manufacturing, supply chain optimization, and predictive maintenance. These applications require two key features to be efficient, that is, real-time processing and connection security and reliability. From this standpoint arise the studies of cyberattack detection in industrial environments. Many methodologies have approached this field using traditional or hybridized machine learning or deep learning algorithms. In this review paper, we explore 36 of the most recent cyber-attack detection systems using metaheuristic models, mainly metaheuristic feature selection (MFS) algorithms. Additionally, we also explore hybrid models of metaheuristics and machine learning or deep learning models that are used to increase the accuracy of the models on various benchmark datasets. Our SLR separates the MFS utilized in this field into four main types, including Swarm Intelligence (SI), Evolutionary Algorithms (EA), Physics-Based (PHY), and Human-Behavior-Inspired (HBI). Our findings showed that SI-MFS dominates the field, with 25/36 case studies proposing it, while EA was proposed in 3/36 and PHY and HBI were each proposed in 2/36. We also demonstrate the most effective methodologies, such as FS-ID, MFS-D, and Novel Hybrid MFS. We also outline potential open challenges and gaps that require resolution.