Nowadays, Internet-of-things (IoT) facilities have been used worldwide in all digital applications. Hence, maintaining the IoT communication system's security range is crucial to enrich the IoT advanced better. However, the harmful attacks can destroy security and degrade the IoT communication channel by making network traffic, system shutdown, and collapse. The present work has introduced a novel Frog Leap-based Hyper-parameter Tuned Deep Neural (FLbHTDN) model to overcome these issues to detect intrusion in the IoT communication paradigm. Hence, the dataset called Nsl-Kdd has been utilized to validate the pressed model. Initially, the preprocessing process functioned to remove the error from the trained dataset. Consequently, the present features in the dataset have been tracked, and the malicious features have been extracted and classified as specific attack classes. The designed model is executed in the Java platform, and the improvement measure of the developed technique has been validated by performing the comparative analysis. The proposed FLbHTDN approach has obtained the finest attack prediction score in less duration than the compared models.