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The EFS-LSTM classifier is evaluated using the classification performance metrics and also compared with all the 41 features without any feature selection as well as with each individual feature selection techniques and classified using LSTM. The performance study showed that the EFS-LSTM model outperforms better with 99.8% accuracy with a higher detection and less false alarm rates.<\/p>","DOI":"10.4018\/ijec.2020100106","type":"journal-article","created":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T13:50:13Z","timestamp":1598449813000},"page":"72-86","source":"Crossref","is-referenced-by-count":10,"title":["EFS-LSTM (Ensemble-Based Feature Selection With LSTM) Classifier for Intrusion Detection System"],"prefix":"10.4018","volume":"16","author":[{"family":"Preethi D.","sequence":"first","affiliation":[{"name":"Vellore Institute of Technology, India"}]},{"given":"Neelu","family":"Khare","sequence":"additional","affiliation":[{"name":"Vellore Institute of Technology, India"}]}],"member":"2432","reference":[{"key":"IJeC.2020100106-0","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.07.005"},{"key":"IJeC.2020100106-1","first-page":"1","article-title":"Applying Long Short-Term Memory Recurrent Neural Network for Intrusion Detection.","volume":"2018","author":"S. 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