Leveraging blockchain for cybersecurity detection using hybridization of prairie dog optimization with differential evolution on internet of things environment

The Internet of Things (IoT) is emerging as a functional occurrence in developing numerous critical applications. These applications rely on centralized storage, raising concerns about confidentiality, security, and single points of failure. Conventional IoT security methods are inadequate to address the growing nature of attacks and threats. Blockchain technology has been employed by many investigators in intrusion detection systems for enhanced detection and monitoring, inhibition of mischievous attacks or activities, and tamper-proof dealings and storage in IoT networks and devices. At present, using artificial intelligence knowledge, mainly deep learning and machine learning approaches, endures the basics to deliver a dynamically improved and up-to-date security method for next-generation IoT systems. This study proposes a Leveraging Blockchain for Cybersecurity Detection Using Golden Jackal Optimization (LBCCD-GJO) method in IoT. The presented LBCCD-GJO method initially applies data pre-processing using min-max normalization to convert input data into a beneficial format. Moreover, the feature selection process is implemented by utilizing the golden jackal optimization (GJO) model. Furthermore, the proposed LBCCD-GJO model employs the gated recurrent unit (GRU) technique for the classification process of cyberattacks. Finally, the hyperparameter selection of the GRU technique is performed by implementing the hybrid of prairie dog optimization with a differential evolution (PDO-DE) technique. An extensive set of simulations was performed to exhibit the promising outcomes of the LBCCD-GJO methodology under the TON_IoT_Train_Test_Network dataset. The experimental validation of the LBCCD-GJO methodology is 99.67% compared to the previous techniques.