{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:14:30Z","timestamp":1777706070022,"version":"3.51.4"},"reference-count":24,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,6,1]]},"abstract":"<jats:p>The evolving new and modern technologies raise the risks in the network which will be affected by several attacks and thus give rise to developing efficient network attack detection and classification methods. Here in this article for predicting and classifying the network attacks, the LSTM neural network with XGBoost is suggested in which the NSL-KDD dataset was utilized to train the LSTM in the study. In the beginning, the unnecessary data and the noisy data will be eliminated using the dataset and the feature subset with the most compelling features will be selected using the feature selection. By utilizing the essential data, the proposed system will be trained and the training parameter values will be modified for maximizing the functionality of the proposed system. Then, the result of the proposed system will be evaluated with some of the existing machine learning and deep learning algorithms such as SVM, LR, RF, DNN, and CNN with the performance metrics like Accuracy, F1 score, Recall, and Precision. It was found that the proposed model outperforms better than the other algorithms as this model is trained with the most important features and due to this, the training time and overfitting of the learning model was reduced thereby increasing the model effectiveness<\/jats:p>","DOI":"10.3233\/jifs-212731","type":"journal-article","created":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T15:28:52Z","timestamp":1642519732000},"page":"971-984","source":"Crossref","is-referenced-by-count":7,"title":["Network attack classification using LSTM with XGBoost feature selection"],"prefix":"10.1177","volume":"43","author":[{"given":"R.","family":"Poornima","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, K.S. Rangasamy College of Technology, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohanraj","family":"Elangovan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, K.S. Rangasamy College of Technology, Tiruchengode, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G.","family":"Nagarajan","sequence":"additional","affiliation":[{"name":"Department of Information Technology, KSR College of Engineering, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-212731_ref1","doi-asserted-by":"crossref","first-page":"4583","DOI":"10.3390\/s20164583","article-title":"A deep learning ensemble for network anomaly and cyber-attack detection","volume":"20","author":"Dutta","year":"2020","journal-title":"Sensors"},{"key":"10.3233\/JIFS-212731_ref2","doi-asserted-by":"crossref","unstructured":"Supriya S. and Samrat T. , Long short-term memory (LSTM) deep learning method for intrusion detection in network security, International Journal of Engineering Research 9 (2020).","DOI":"10.17577\/IJERTV9IS061016"},{"key":"10.3233\/JIFS-212731_ref3","doi-asserted-by":"crossref","first-page":"22616","DOI":"10.1109\/ACCESS.2021.3056482","article-title":"LSTM-CGAN: Towards generating low-rate DDoS adversarial samples for block chain-based wireless network detection models","volume":"9","author":"Liu","year":"2021","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-212731_ref5","doi-asserted-by":"crossref","unstructured":"Mohammad M. , Hossain S. and Hisham M. , Haddad.: A Transfer Learning with Deep Neural Network Approach for Network Intrusion Detection, International Journal of Intelligent Computing Research (IJICR) 12 (2021).","DOI":"10.20533\/ijicr.2042.4655.2021.0132"},{"key":"10.3233\/JIFS-212731_ref6","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/TETCI.2017.2772792","article-title":"A Deep Learning Approach to Network Intrusion Detection","volume":"2","author":"Shone","year":"2018","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"10.3233\/JIFS-212731_ref7","doi-asserted-by":"crossref","first-page":"3414","DOI":"10.3390\/app9163414","article-title":"An LSTM-based deep learning approach for classifying malicious traffic at the packet level","volume":"9","author":"Hwang","year":"2019","journal-title":"Applied Sciences"},{"key":"10.3233\/JIFS-212731_ref8","doi-asserted-by":"crossref","first-page":"183207","DOI":"10.1109\/ACCESS.2019.2959131","article-title":"A novel multimodal-sequential approach based on multi-view features for network intrusion detection","volume":"7","author":"He","year":"2019","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-212731_ref9","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1080\/21642583.2019.1620658","article-title":"An ensemble feature selection method for high-dimensional data based on sort aggregation","volume":"7","author":"Wang","year":"2019","journal-title":"Systems Science & Control Engineering"},{"key":"10.3233\/JIFS-212731_ref10","doi-asserted-by":"crossref","first-page":"73271","DOI":"10.1109\/ACCESS.2019.2920655","article-title":"OFS-NN: An effective phishing websites detection model based on optimal feature selection and neural network","volume":"7","author":"Zhu","year":"2019","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-212731_ref11","doi-asserted-by":"crossref","unstructured":"Sarumi O.A. , Adetunmbi A.O. and Adetoye F.A. , Discovering computer networks intrusion using data analytics and machine intelligence, Scientific African 9 (2020).","DOI":"10.1016\/j.sciaf.2020.e00500"},{"key":"10.3233\/JIFS-212731_ref12","doi-asserted-by":"crossref","unstructured":"Sarker I.H. , Abushark Y.B. and Alsolami F. , and A.I Khan, IntruDTree: A machine learning based cyber security intrusion detection model, Symmetry 12 (2020).","DOI":"10.20944\/preprints202004.0481.v1"},{"key":"10.3233\/JIFS-212731_ref15","first-page":"8278","article-title":"Network intrusion detection using Deep Learning techniques","volume":"29","author":"Venkata","year":"2020","journal-title":"International Journal of Advanced Science and Technology"},{"key":"10.3233\/JIFS-212731_ref16","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"},{"key":"10.3233\/JIFS-212731_ref17","doi-asserted-by":"crossref","unstructured":"Pan Y. , Sun F. , Teng Z. , White J. , Schmidt D.C. , Staples J. and Krause L. , Detecting web attacks with end-to-end deep learning, Journal of Internet Services and Applications 10 (2019).","DOI":"10.1186\/s13174-019-0115-x"},{"key":"10.3233\/JIFS-212731_ref18","doi-asserted-by":"crossref","first-page":"4396","DOI":"10.3390\/app9204396","article-title":"Machine learning and deep learning methods for intrusion detection systems: A survey","volume":"9","author":"Liu","year":"2019","journal-title":"Applied Sciences"},{"key":"10.3233\/JIFS-212731_ref19","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.icte.2018.04.003","article-title":"Intelligent intrusion detection systems using artificial neural networks","volume":"4","author":"Shenfield","year":"2018","journal-title":"ICT Express"},{"key":"10.3233\/JIFS-212731_ref21","doi-asserted-by":"crossref","unstructured":"Kasongo S.M. and Sun Y. , Performance analysis of intrusion detection systems using a feature selection method on the UNSW-NB15 dataset, Journal of Big Data 7 (2020).","DOI":"10.1186\/s40537-020-00379-6"},{"key":"10.3233\/JIFS-212731_ref23","doi-asserted-by":"crossref","first-page":"12499","DOI":"10.1007\/s00521-020-04708-x","article-title":"An efficient xgboost\u2013dnn-based classification model for network intrusion detection system","volume":"32","author":"Devan","year":"2020","journal-title":"Neural Computing and Applications"},{"key":"10.3233\/JIFS-212731_ref26","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1109\/OJITS.2020.3043066","article-title":"Anomaly detection for controller area networks using long short-term memory","volume":"1","author":"Tanksale","year":"2020","journal-title":"IEEE Open Journal of Intelligent Transportation Systems"},{"issue":"2021","key":"10.3233\/JIFS-212731_ref27","first-page":"87079","article-title":"A spectrogram image-based network anomaly detection system using deep convolutional neural network","volume":"9","author":"Khan","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-212731_ref28","doi-asserted-by":"crossref","first-page":"87936","DOI":"10.1109\/ACCESS.2021.3089586","article-title":"A new malware classification framework based on deep learning algorithms","volume":"9","author":"Aslan","year":"2021","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-212731_ref29","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1109\/TNSM.2020.2971776","article-title":"Lucid: A practical, lightweight deep learning solution for DDoS attack detection","volume":"17","author":"Doriguzzi-Corin","year":"2020","journal-title":"IEEE Trans Netw Serv Manag"},{"key":"10.3233\/JIFS-212731_ref30","doi-asserted-by":"crossref","first-page":"29575","DOI":"10.1109\/ACCESS.2020.2972627","article-title":"BAT: Deep learning methods on network intrusion detection using NSL-KDD dataset","volume":"8","author":"Su","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-212731_ref31","doi-asserted-by":"crossref","first-page":"30373","DOI":"10.1109\/ACCESS.2019.2899721","article-title":"TSDL: A two-stage deep learning model for efficient network intrusion detection","volume":"7","author":"Khan","year":"2019","journal-title":"IEEE Access"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-212731","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:45:55Z","timestamp":1777455955000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-212731"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,1]]},"references-count":24,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/jifs-212731","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,1]]}}}