{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:27:01Z","timestamp":1772303221618,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,7,2]],"date-time":"2022-07-02T00:00:00Z","timestamp":1656720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Suranaree University of Technology (SUT) Research and Development Funds"},{"name":"Thailand Science Research and Innovation (TSRI)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Network data traffic is increasing with expanded networks for various applications, with text, image, audio, and video for inevitable needs. Network traffic pattern identification and analysis of traffic of data content are essential for different needs and different scenarios. Many approaches have been followed, both before and after the introduction of machine and deep learning algorithms as intelligence computation. The network traffic analysis is the process of incarcerating traffic of a network and observing it deeply to predict what the manifestation in traffic of the network is. To enhance the quality of service (QoS) of a network, it is important to estimate the network traffic and analyze its accuracy and precision, as well as the false positive and negative rates, with suitable algorithms. This proposed work is coining a new method using an enhanced deep reinforcement learning (EDRL) algorithm to improve network traffic analysis and prediction. The importance of this proposed work is to contribute towards intelligence-based network traffic prediction and solve network management issues. An experiment was carried out to check the accuracy and precision, as well as the false positive and negative parameters with EDRL. Also, convolutional neural network (CNN) machines and deep learning algorithms have been used to predict the different types of network traffic, which are labeled text-based, video-based, and unencrypted and encrypted data traffic. The EDRL algorithm has outperformed with mean Accuracy (97.20%), mean Precision (97.343%), mean false positive (2.657%) and mean false negative (2.527%) than the CNN algorithm.<\/jats:p>","DOI":"10.3390\/s22135006","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T20:59:18Z","timestamp":1656968358000},"page":"5006","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A Novel Method for Improved Network Traffic Prediction Using Enhanced Deep Reinforcement Learning Algorithm"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6166-1385","authenticated-orcid":false,"given":"Nagaiah Mohanan","family":"Balamurugan","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Sriperumbudur 602117, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7293-9020","authenticated-orcid":false,"given":"Malaiyalathan","family":"Adimoolam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam 602105, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8579-5444","authenticated-orcid":false,"given":"Mohammed H.","family":"Alsharif","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, Seoul 05006, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7108-9263","authenticated-orcid":false,"given":"Peerapong","family":"Uthansakul","sequence":"additional","affiliation":[{"name":"School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s40537-019-0176-5","article-title":"Data mining approach for predicting the daily Internet data traffic of a smart university","volume":"6","author":"Adekitan","year":"2019","journal-title":"J. Big Data"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Alsharif, M.H., Younes, M.K., and Kim, J. (2019). Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea. Symmetry, 11.","DOI":"10.3390\/sym11020240"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alsharif, M.H., Kelechi, A.H., Yahya, K., and Chaudhry, S.A. (2020). Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends. Symmetry, 12.","DOI":"10.3390\/sym12010088"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, W., Bai, Y., Yu, C., Gu, Y., Feng, P., Wang, X., and Wang, R. (2018, January 23\u201327). A network traffic flow prediction with deep learning approach for large-scale metropolitan area network. Proceedings of the 2018 IEEE\/IFIP Network Operations and Management Symposium (NOMS 2018), Taipei, Taiwan.","DOI":"10.1109\/NOMS.2018.8406252"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cortez, P., Rio, M., Rocha, M., and Sousa, P. (2006, January 16\u201321). Internet Traffic Forecasting using Neural Networks. Proceedings of the 2006 IEEE International Joint Conference on Neural Network Proceedings, Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2006.247142"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Esposito, A., Faudez-Zanuy, M., Morabito, F., and Pasero, E. (2018). An Application of Internet Traffic Prediction with Deep Neural Network. Multidisciplinary Approaches to Neural Computing, Springer. Smart Innovation, Systems and Technologies.","DOI":"10.1007\/978-3-319-56904-8"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Vinayakumar, R., Soman, K.P., and Poornachandran, P. (2017, January 13\u201316). Applying deep learning approaches for network traffic prediction. Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India.","DOI":"10.1109\/ICACCI.2017.8126198"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Park, J., Yi, D., and Ji, S. (2020). Analysis of Recurrent Neural Network and Predictions. Symmetry, 12.","DOI":"10.3390\/sym12040615"},{"key":"ref_9","first-page":"28","article-title":"Computer network traffic prediction: A comparison between traditional and deep learning neural networks","volume":"3","author":"Oliveira","year":"2016","journal-title":"Int. J. Big Data Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yoo, W., and Sim, A. (2015, January 16\u201319). Network bandwidth utilization forecast model on high bandwidth networks. Proceedings of the 2015 International Conference on Computing, Networking and Communications (ICNC), Garden Grove, CA, USA.","DOI":"10.1109\/ICCNC.2015.7069393"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"518","DOI":"10.3390\/telecom2040029","article-title":"A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks","volume":"2","author":"Chen","year":"2021","journal-title":"Telecom"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rezaei, S., and Liu, X. (2020, January 3\u20136). Multitask learning for network traffic classification. Proceedings of the International Conference on Computer Communications and Networks (ICCCN), Honolulu, HI, USA.","DOI":"10.1109\/ICCCN49398.2020.9209652"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1007\/s00500-019-04030-2","article-title":"Deep packet: A novel approach for encrypted traffic classification using deep learning","volume":"24","author":"Lotfollahi","year":"2020","journal-title":"Soft Comput."},{"key":"ref_14","first-page":"42","article-title":"Network traffic classifier with convolutional and recurrent neural networks for internet of things","volume":"5","author":"Carro","year":"2017","journal-title":"IEEE Access"},{"key":"ref_15","first-page":"182","article-title":"Deep-Full-Range: A deep learning based network encrypted traffic classification and intrusion detection framework","volume":"7","author":"Zeng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.comnet.2020.107557","article-title":"Online classification of user activities using machine learning on network traffic","volume":"181","author":"Labayen","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_17","first-page":"216","article-title":"Application-based online traffic classification with deep learning models on sdn networks","volume":"5","author":"Chang","year":"2020","journal-title":"Adv. Technol. Innov."},{"key":"ref_18","unstructured":"Gil, G.D., Lashkari, A.H., Mamun, M., and Ghorbani, A.A. (2016, January 19\u201321). Characterization of Encrypted and VPN Traffic Using Time-Related Features. Proceedings of the 2nd International Conference on Information Systems Security and Privacy (ICISSP 2016), Rome, Italy."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TCIAIG.2012.2186810","article-title":"A survey of Monte Carlo tree search methods","volume":"4","author":"Browne","year":"2012","journal-title":"IEEE Trans. Comput. Intell. AI Games"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ahmed, A.A., and Agunsoye, G. (2021). A Real-Time Network Traffic Classifier for Online Applications Using Machine Learning. Algorithms, 14.","DOI":"10.3390\/a14080250"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Troia, S., Alvizu, R., Zhou, Y., Maier, G., and Pattavina, A. (2018, January 1\u20135). Deep Learning-Based Traffic Prediction for Network Optimization. Proceedings of the 2018 20th International Conference on Transparent Optical Networks (ICTON), Bucharest, Romania.","DOI":"10.1109\/ICTON.2018.8473978"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1109\/COMST.2018.2883147","article-title":"Towards the deployment of Machine Learning solutions in network traffic classification: A systematic survey","volume":"21","author":"Pacheco","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mohammed, A.R., Mohammed, S.A., and Shirmohammadi, S. (2019, January 8\u201310). Machine Learning and Deep Learning Based Traffic Classification and Prediction in Software Defined Networking. Proceedings of the 2019 IEEE International Symposium on Measurements & Networking (M&N), Catania, Italy.","DOI":"10.1109\/IWMN.2019.8805044"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"114885","DOI":"10.1016\/j.eswa.2021.114885","article-title":"Multi class SVM algorithm with active learning for network traffic classification","volume":"176","author":"Dong","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Balamurugan, N.M., Kannadasan, R., Alsharif, M.H., and Uthansakul, P. (2022). A Novel Forward-Propagation Workflow Assessment Method for Malicious Packet Detection. Sensors, 22.","DOI":"10.3390\/s22114167"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/5006\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:42:08Z","timestamp":1760139728000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/5006"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,2]]},"references-count":25,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22135006"],"URL":"https:\/\/doi.org\/10.3390\/s22135006","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,2]]}}}