International Journal of Wireless and Ad Hoc Communication IJWAC 2692-4056 10.54216/IJWAC https://www.americaspg.com/journals/show/1470 2019 2019 Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharqiyah, 44519, Egypt Waleed Abd Elkhalik Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharqiyah, 44519, Egypt Ibrahim Elhenawy Because of the lightning-fast expansion of the Internet of Things (IoT) technologies, an enormous amount of data has been produced. This traffic can be mined for information that can be used to identify and avoid intrusions into IoT networks. Despite the significant efforts that have been put into labeling Internet of Things traffic records, the total number of labeled records is still quite low, which makes it more difficult to detect intrusions. This study introduces a semi-supervised deep learning approach for intrusion detection (S2T-Net), in which we propose a temporal transformer module to empower the model to learn valuable interactions in cellular data. An improved spatial transformer is presented to capture local representation in the cellular traffic flow. At the same time, a multilevel semi-supervised training technique is used to account for the consecutive structure of the IoT traffic information. In order to provide effective real-time threat intelligence, the suggested S2T-Net can be tightly coupled into a cellular IoT network. Last but not least, empirical assessments on two current databases (CIC-IDS2017 and CIC-IDS2018) show that S2T-Net boosts intrusion detection accuracy and resilience while retaining resource-efficient computing. 2022 2022 56 68 10.54216/IJWAC.040106 https://www.americaspg.com/articleinfo/20/show/1470