aspg-18-1126-1792025573 20261015005253 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2022 6 1 Tarek Gaber School of Science, Engineering and Environment, University of Salford, UK Joseph Bamidele Awotunde Department of Computer Science, University of Ilorin, Ilorin, 240003, Nigeria Chin-Shiuh Shieh Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Taiwan Accurate and prompt detection of security attacks in the Industrial Internet of Things (IIoT) is important to reduce security risks. Since a massive number of IoT devices are placed over the globe and the quantity gets increased, an effective security solution is necessary. A botnet is a computer network comprising numerous hosts executing on standalone software. In this view, this article develops a novel Glowworm Swarm Optimization Driven Gated Recurrent Unit Enabled Botnet Detection (GSOGRU-BD) model in IIoT Environment. The presented GSOGRU-BD model intends to effectually identify the presence of botnet attacks in the IIoT environment. To do so, the GSOGRU-BD model initially pre-processed the input data to get rid of missing values. In addition, the GSOGRU-BD model involves the GRU model for the effective recognition and classification of botnets. Besides, the GSO algorithm is used for optimal hyperparameter tuning of the GRU model. Comparative experimental validation of the GSOGRU-BD model is tested using a benchmark dataset and the results reported the better outcomes for the GSOGRU-BD model. 2022 30 40 1126 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.060103 https://americaspg.com/journal/18/article/1126 https://americaspg.com/storage/7096.pdf