To protect network infrastructure from new vulnerabilities and security dangers caused by the rapid growth of Internet-of-Things (IoT) devices, robust and adaptable Intrusion Detection Systems (IDS) are necessary. Due to their limited scalability and reactivity to different attack patterns, conventional intrusion detection systems (IDS) struggle to meet the unique demands of Internet-of-Things (IoT) networks. The novel intrusion detection system introduced in this paper is based on deep learning and is tailor-made for Internet-of-Things (IoT) environments. It employs complex neural network topologies to enhance the accuracy and efficiency of detection. Regarding the massive amount and variety of data generated by IoT devices, our suggested method improves performance without compromising detection accuracy by combining feature selection and dimensionality reduction strategy. Standard IoT network datasets were used for training and validation, with several assaults implemented to ensure comprehensive threat coverage and practical applicability. The results of the experiments show that the proposed system outperforms the state-of-the-art machine learning-based intrusion detection systems in detection accuracy, false positive rates, and scalability in contexts with limited resources for the Internet of Things.