Research Article Deep Neural Network-Based Intrusion Detection System through PCA Shoayee Dlaim Alotaibi, 1 Kusum Yadav, 1 Arwa N. Aledaily, 1 Lulwah M Alkwai, 1 Alaa Kamal Yousef Dafhalla, 1 Shahad Almansour , 1 and Velmurugan Lingamuthu 2 1 College of Computer Science and Engineering, University of Ha’il, Kingdom of Ha’il, Saudi Arabia 2 Department of Computer Science, School of Informatics and Electrical Engineering, Hachalu Hundesa Campus, Ambo University, Ambo, Ethiopia Correspondence should be addressed to Velmurugan Lingamuthu; velmurugan.lingamuthu@ambou.edu.et Received 3 March 2022; Revised 2 April 2022; Accepted 8 April 2022; Published 9 May 2022 Academic Editor: Amandeep Kaur Copyright © 2022 Shoayee Dlaim Alotaibi et al. 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. Today, challenges such as a high false-positive rate, a low detection rate, a slow processing speed, and a big feature dimension are all part of intrusion detection. To address these issues, decision trees (DTs), deep neural networks (DNNs), and principal component analysis (PCA) are available. rough a higher detection rate and a lower false-positive rate, the research-based intrusion detection model DT-PCA-DNN increases the processing speed of intrusion detection systems (IDSs). To minimize the overall data volume and accelerate processing, DT is used to initially differentiate the data. Differentiate DTs save the temporary training sample set for intrusion data in order to retrain and optimize the DT and DNN, treat the DT judges as standard data, and delete the added average data. After signing, we should lower the dimension of the data using PCA and then submit the data to DNN for secondary discrimination. However, DT employs a shallow structure in order to prevent an excessive quantity of average numbers from being interpreted as intrusion data. As a result, additional DNN secondary processing cannot effectively increase the accuracy. DNN accelerates data processing by utilizing the ReLU activation function from the simplified neural network calculation approach and the faster convergence ADAM optimization algorithm. Class two and five trials on the NSL-KDD dataset demonstrate that the proposed model is capable of achieving high detection accuracy when compared to other deep learning-based intrusion detection approaches. Simultaneously, it has a faster detection rate, which effectively solves the real-time intrusion detection problem. 1. Introduction Communication systems and network entrances are al- ways faced with network attacks from the outside or even within their systems and are not like single attacks in the immature period of the network. Today, most of the in- trusion behaviors are of various types and are developing in a mixed situation. Development is getting harder. According to the relevant literature, the Yahoo data breach caused a loss of 350 million US dollars and the “Bitcoin” breach caused a loss of about 70 million US dollars [1]. Based on intrusion behavior, the intrusion detection can be divided into network-based intrusion detection system (NIDS) and host-based intrusion de- tection system (HIDS) [2]. Various log files, disk resource information, and system information are used to detect intrusion behavior, while NIDS judges whether there is intrusion behavior by detecting the data packets in and out of the local network data flow. Machine learning, as a very popular algorithm tool in recent years, deserves experts and scholars to try its application in intrusion detection [3]. Especially in recent years, the application of machine learning in intrusion detection has appeared in people’s field of vision; the support vector machine (SVM) to neural network (NN) to random forest (RF) has their applications in intrusion detection. Hindawi Mathematical Problems in Engineering Volume 2022, Article ID 6488571, 9 pages https://doi.org/10.1155/2022/6488571