aspg-2-793-1791416917 20261007234837 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cybersecurity and Information Management JCIM 2769-7851 2690-6775 2021 8 1 Mohammad Hammoudeh Manchester Metropolitan University, UK Saeed M. Aljaberi Artificial Intelligence department, Dubai Police, Dubai, UAE The Internet of Things (IoT) has become a hot popular topic for building a smart environment. At the same time, security and privacy are treated as significant problems in the real-time IoT platform. Therefore, it is highly needed to design intrusion detection techniques for accomplishing security in IoT. With this motivation, this study designs a novel flower pollination algorithm (FPA) based feature selection with a gated recurrent unit (GRU) model, named FPAFS-GRU technique for intrusion detection in the IoT platform. The proposed FPAFS-GRU technique is mainly designed to determine the presence of intrusions in the network. The FPAFS-GRU technique involves the design of the FPAFS technique to choose an optimal subset of features from the networking data. Besides, a deep learning based GRU model is applied as a classification tool to identify the network intrusions. An extensive experimental analysis takes place on KDDCup 1999 dataset, and the results are investigated under different dimensions. The resultant simulation values demonstrated the betterment of the FPAFS-GRU technique with a higher detection rate of 0.9976. 2021 17 25 793 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCIM.080102 https://www.americaspg.com/journal/2/article/793 https://www.americaspg.com/storage/2902.pdf