{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T19:51:17Z","timestamp":1784404277488,"version":"3.55.0"},"reference-count":22,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T00:00:00Z","timestamp":1664928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, network traffic contains a lot of feature information. If there are too many redundant features, the computational cost of the algorithm will be greatly increased. This paper proposes an anomalous network traffic detection method based on Elevated Harris Hawks optimization. This method is easier to identify redundant features in anomalous network traffic, reduces computational overhead, and improves the performance of anomalous traffic detection methods. By enhancing the random jump distance function, escape energy function, and designing a unique fitness function, there is a unique anomalous traffic detection method built using the algorithm and the neural network for anomalous traffic detection. This method is tested on three public network traffic datasets, namely the UNSW-NB15, NSL-KDD, and CICIDS2018. The experimental results show that the proposed method does not only significantly reduce the number of features in the dataset and computational overhead, but also gives better indicators for every test.<\/jats:p>","DOI":"10.3390\/s22197548","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T05:12:21Z","timestamp":1665378741000},"page":"7548","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Anomalous Network Traffic Detection Method Based on an Elevated Harris Hawks Optimization Method and Gated Recurrent Unit Classifier"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9395-2331","authenticated-orcid":false,"given":"Yao","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Data Science and Technology, Heilongjiang University, Harbin 150000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunying","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Data Science and Technology, Heilongjiang University, Harbin 150000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongchen","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Data Science and Technology, Heilongjiang University, Harbin 150000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Data Science and Technology, Heilongjiang University, Harbin 150000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haofang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Data Science and Technology, Heilongjiang University, Harbin 150000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Almomani, O. (2020). A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms. Symmetry, 12.","DOI":"10.3390\/sym12061046"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.cose.2019.05.022","article-title":"A novel approach to intrusion detection using SVM ensemble with feature augmentation","volume":"86","author":"Gu","year":"2019","journal-title":"Comput. Secur."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"109472","DOI":"10.1016\/j.commatsci.2019.109472","article-title":"A steel property optimization model based on the XGBoost algorithm and improved PSO","volume":"174","author":"Song","year":"2020","journal-title":"Comput. Mater. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"115452","DOI":"10.1016\/j.eswa.2021.115452","article-title":"Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost","volume":"183","author":"Diniz","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_5","first-page":"420","article-title":"Feature selection for intrusion detection system using ant colony optimization","volume":"18","author":"Aghdam","year":"2016","journal-title":"Int. J. Netw. Secur."},{"key":"ref_6","first-page":"541","article-title":"Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms","volume":"31","author":"Mazini","year":"2019","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"31711","DOI":"10.1109\/ACCESS.2019.2903723","article-title":"Intrusion detection for IoT based on improved genetic algorithm and deep belief network","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, L., Fan, X., and Xu, C. (2017, January 18\u201320). A fusion financial prediction strategy based on RNN and representative pattern discovery. Proceedings of the 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), Taipei, Taiwan.","DOI":"10.1109\/PDCAT.2017.00024"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1007\/s00521-010-0487-0","article-title":"Intrusion detection using reduced-size RNN based on feature grouping","volume":"21","author":"Sheikhan","year":"2012","journal-title":"Neural Comput. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Agarap, A.F.M. (2018, January 26\u201328). A neural network architecture combining gated recurrent unit (GRU) and support vector machine (SVM) for intrusion detection in network traffic data. Proceedings of the 2018 10th International Conference on Machine Learning and Computing, Macau, China.","DOI":"10.1145\/3195106.3195117"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, H., Kang, C., and Xiao, Y. (2021). Research on Network Security Situation Awareness Based on the LSTM-DT Model. Sensors, 21.","DOI":"10.3390\/s21144788"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sak, H., Senior, A., and Beaufays, F. (2014). Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. arXiv.","DOI":"10.21437\/Interspeech.2014-80"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, Y., and Lu, Y. (2019, January 21\u201322). LSTM-BA: DDoS detection approach combining LSTM and Bayes. Proceedings of the 2019 Seventh International Conference on Advanced Cloud and Big Data (CBD), Suzhou, China.","DOI":"10.1109\/CBD.2019.00041"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","article-title":"Harris hawks optimization: Algorithm and applications","volume":"97","author":"Heidari","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"118778","DOI":"10.1016\/j.jclepro.2019.118778","article-title":"Parameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic drifts","volume":"244","author":"Chen","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"580","DOI":"10.5937\/vojtehg66-16670","article-title":"Review of KDD Cup 99, NSL-KDD and Kyoto 2006+ datasets","volume":"66","year":"2018","journal-title":"Vojnoteh. Glas. Tech. Cour."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1080\/19393555.2015.1125974","article-title":"The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set","volume":"25","author":"Moustafa","year":"2016","journal-title":"Inf. Secur. J. A Glob. Perspect."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","article-title":"The whale optimization algorithm","volume":"95","author":"Mirjalili","year":"2016","journal-title":"Adv. Eng. Softw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s11721-007-0002-0","article-title":"Particle swarm optimization","volume":"1","author":"Poli","year":"2007","journal-title":"Swarm Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"41525","DOI":"10.1109\/ACCESS.2019.2895334","article-title":"Deep learning approach for intelligent intrusion detection system","volume":"7","author":"Vinayakumar","year":"2019","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7548\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:46:54Z","timestamp":1760143614000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7548"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,5]]},"references-count":22,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22197548"],"URL":"https:\/\/doi.org\/10.3390\/s22197548","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,5]]}}}