Research Article Intrusion Detection System to Advance Internet of Things Infrastructure-Based Deep Learning Algorithms Hasan Alkahtani 1 and Theyazn H. H. Aldhyani 2 1 College of Computer Science and Information Technology, King Faisal University, P. O. Box 400, Al-Ahsa, Saudi Arabia 2 Community College of Abqaiq, King Faisal University, P. O. Box 400, Al-Ahsa, Saudi Arabia Correspondence should be addressed to eyazn H. H. Aldhyani; taldhyani@kfu.edu.sa Received 28 February 2021; Revised 23 March 2021; Accepted 17 April 2021; Published 7 July 2021 Academic Editor: M. Irfan Uddin Copyright © 2021 Hasan Alkahtani and eyazn H. H. Aldhyani. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Smart grids, advanced information technology, have become the favored intrusion targets due to the Internet of ings (IoT) using sensor devices to collect data from a smart grid environment. ese data are sent to the cloud, which is a huge network of super servers that provides different services to different smart infrastructures, such as smart homes and smart buildings. ese can provide a large space for attackers to launch destructive cyberattacks. e novelty of this proposed research is the development of a robust framework system for detecting intrusions based on the IoTenvironment. An IoTID20 dataset attack was employed to develop the proposed system; it is a newly generated dataset from the IoT infrastructure. In this framework, three advanced deep learning algorithms were applied to classify the intrusion: a convolution neural network (CNN), a long short-term memory (LSTM), and a hybrid convolution neural network with the long short-term memory (CNN-LSTM) model. e complexity of the network dataset was dimensionality reduced, and to improve the proposed system, the particle swarm optimization method (PSO) was used to select relevant features from the network dataset. e obtained features were processed using deep learning al- gorithms. e experimental results showed that the proposed systems achieved accuracy as follows: CNN � 96.60%, LSTM � 99.82%, and CNN-LSTM � 98.80%. e proposed framework attained the desired performance on a new variable dataset, and the system will be implemented in our university IoT environment. e results of comparative predictions between the proposed framework and existing systems showed that the proposed system more efficiently and effectively enhanced the security of the IoT environment from attacks. e experimental results confirmed that the proposed framework based on deep learning algorithms for an intrusion detection system can effectively detect real-world attacks and is capable of enhancing the security of the IoT environment. 1. Introduction Currently, there are more than 25 billion devices connected to the Internet worldwide, three times as many human beings [1–3]. e Internet of ings (IoT) is based on interconnected smart devices, and different services are used to integrate them into a single network. is allows the smart devices to gather sensitive information and carry out im- portant functions, and these devices connect and commu- nicate with each other at high speeds and make decisions according to indicator information. e IoT environment uses cloud services as a backend for processing information and maintaining remote control. Client users use mobile applications or web services to access data and control the devices. e IoTinfrastructure uses large numbers of sensors to extract significant information, and this information is analyzed by artificial intelligence algorithms [4, 5]. Intrusion detection systems (IDSs) are the technical, regulatory, and administrative means used to prevent un- authorized use, abuse, and recovery of electronic informa- tion and communication systems and the information they contain, aimed at ensuring the availability and continuity of the work of the information systems and enhancing the protection, confidentiality, and privacy of personal data by taking all measures. Cybersecurity is the practice of defending computers, servers, mobile devices, electronic Hindawi Complexity Volume 2021, Article ID 5579851, 18 pages https://doi.org/10.1155/2021/5579851