{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T07:55:35Z","timestamp":1781596535813,"version":"3.54.5"},"reference-count":32,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS-1900187"],"award-info":[{"award-number":["CNS-1900187"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>An intrusion detection system (IDS) identifies whether the network traffic behavior is normal or abnormal or identifies the attack types. Recently, deep learning has emerged as a successful approach in IDSs, having a high accuracy rate with its distinctive learning mechanism. In this research, we developed a new method for intrusion detection to classify the NSL-KDD dataset by combining a genetic algorithm (GA) for optimal feature selection and long short-term memory (LSTM) with a recurrent neural network (RNN). We found that using LSTM-RNN classifiers with the optimal feature set improves intrusion detection. The performance of the IDS was analyzed by calculating the accuracy, recall, precision, f-score, and confusion matrix. The NSL-KDD dataset was used to analyze the performances of the classifiers. An LSTM-RNN was used to classify the NSL-KDD datasets into binary (normal and abnormal) and multi-class (Normal, DoS, Probing, U2R, and R2L) sets. The results indicate that applying the GA increases the classification accuracy of LSTM-RNN in both binary and multi-class classification. The results of the LSTM-RNN classifier were also compared with the results using a support vector machine (SVM) and random forest (RF). For multi-class classification, the classification accuracy of LSTM-RNN with the GA model is much higher than SVM and RF. For binary classification, the classification accuracy of LSTM-RNN is similar to that of RF and higher than that of SVM.<\/jats:p>","DOI":"10.3390\/info11050243","type":"journal-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T03:29:39Z","timestamp":1588562979000},"page":"243","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":124,"title":["Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7273-5274","authenticated-orcid":false,"given":"Pramita Sree","family":"Muhuri","sequence":"first","affiliation":[{"name":"Department of Computer Science, North Carolina A &amp; T State University, Greensboro, NC 27411, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1169-4717","authenticated-orcid":false,"given":"Prosenjit","family":"Chatterjee","sequence":"additional","affiliation":[{"name":"Department of Computer Science, North Carolina A &amp; T State University, Greensboro, NC 27411, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohong","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, North Carolina A &amp; T State University, Greensboro, NC 27411, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaushik","family":"Roy","sequence":"additional","affiliation":[{"name":"Department of Computer Science, North Carolina A &amp; T State University, Greensboro, NC 27411, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Albert","family":"Esterline","sequence":"additional","affiliation":[{"name":"Department of Computer Science, North Carolina A &amp; T State University, Greensboro, NC 27411, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1109\/TSE.1987.232894","article-title":"An intrusion-detection model","volume":"13","author":"Denning","year":"1987","journal-title":"IEEE Trans. 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