Our daily lives have been profoundly changed over the past few years owing to the growing presence of the Internet of Things (IoT). Importantly, IoT makes our lives more convenient, simpler, and more efficient; however, gadgets are vulnerable to a wide variety of cyberattacks due to the lack of robust security mechanisms and hardware security support. This paper presents an alternative deep learning model known as DeepAK-IoT to detect cyberattacks against IoT devices. DeepAK-IoT uses three blocks as its foundation: the residual-based-spatial representation (RSR) block, the temporal representation block (TRB), and the detection block (DB). The RSR block uses five residual blocks to extract a feature representation from the output of the preceding layer. The four convolutional layers are connected in parallel with a skip connection within each block to avoid vanishing or exploding gradients. Then, the second block uses the extracted spatial representation to learn a temporal representation to detect cyber threats. The final block decides how to classify the input record. We evaluated the accuracy and generalization ability of DeepAK-IoT using three well-known public datasets: TON-IoT, Edge-IIoTset, and UNSW-NB15. The proposed model was compared to three state-of-the-art deep learning models to demonstrate its effectiveness in detecting cyber threats in IoT systems. According to the experimental results, DeepAK-IoT was found to be a powerful alternative model for managing cyber threats in IoT networks, as it provided 90.57% accuracy for TON IoT, 94.96% for Edge-IIoTset, and 98.41% for UNSW NB15.