Research Article A Deep Learning-Based Framework for Feature Extraction and Classification of Intrusion Detection in Networks Muhammad Naveed , 1 Fahim Arif , 2 Syed Muhammad Usman , 3 Aamir Anwar , 4 Myriam Hadjouni , 5 Hela Elmannai, 6 Saddam Hussain , 7 Syed Sajid Ullah , 8 and Fazlullah Umar 9 1 Department of Computer Science, SZABIST, Islamabad, Pakistan 2 Department of Computer Software Engineering, MCS, NUST, Islamabad, Pakistan 3 Department of Creative Technologies, Air University, Islamabad, Pakistan 4 School of Computing and Engineering, The University of West London, UK 5 Department of Computer Sciences, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia 6 Department of Information Technology, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia 7 School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei Darussalam 8 Department of Information and Communication Technology, University of Agder (UiA), N-4898 Grimstad, Norway 9 Department of Information Technology, Khana-e-Noor University, Pol-e-Mahmood Khan, Shashdarak, 1001 Kabul, Afghanistan Correspondence should be addressed to Saddam Hussain; saddam_1993@hotmail.com, Syed Sajid Ullah; sajidullah718@gmail.com, and Fazlullah Umar; fazlullahumer@gmail.com Received 28 April 2022; Accepted 1 July 2022; Published 8 August 2022 Academic Editor: Kuruva Lakshmanna Copyright © 2022 Muhammad Naveed et al. This 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. An intrusion detection system, often known as an IDS, is extremely important for preventing attacks on a network, violating network policies, and gaining unauthorized access to a network. The effectiveness of IDS is highly dependent on data preprocessing techniques and classification models used to enhance accuracy and reduce model training and testing time. For the purpose of anomaly identification, researchers have developed several machine learning and deep learning-based algorithms; nonetheless, accurate anomaly detection with low test and train times remains a challenge. Using a hybrid feature selection approach and a deep neural network- (DNN-) based classifier, the authors of this research suggest an enhanced intrusion detection system (IDS). In order to construct a subset of reduced and optimal features that may be used for classification, a hybrid feature selection model that consists of three methods, namely, chi square, ANOVA, and principal component analysis (PCA), is applied. These methods are referred to as “the big three.” On the NSL-KDD dataset, the suggested model receives training and is then evaluated. The proposed method was successful in achieving the following results: a reduction of input data by 40%, an average accuracy of 99.73%, a precision score of 99.75%, an F1 score of 99.72%, and an average training and testing time of 138% and 2.7 seconds, respectively. The findings of the experiments demonstrate that the proposed model is superior to the performance of the other comparison approaches. 1. Introduction There has been a discernible increase in the volume of traffic on the network. On the other hand, the number of potential infiltration threats has grown and their level of sophistica- tion has also improved. Communication that is reliant on networks is now susceptible to attacks from both the outside and the inside. It is quite difficult to check incoming traffic since there is a large volume of traffic and a high number of attacks, which also increases the amount of time and Hindawi Wireless Communications and Mobile Computing Volume 2022, Article ID 2215852, 11 pages https://doi.org/10.1155/2022/2215852