NSGTO‐LSTM: Niche‐strategy‐based gorilla troops optimization and long short‐term memory network intrusion detection model

Abstract In recent decades, the rapid growth of the Internet of Things (IoT) has highlighted several network security problems. In this study, an efficient intrusion detection (ID) system is implemented by using both machine learning and data mining concepts for detecting intrusion patterns. During the initial phase, the intrusion data are collected from NSL‐KDD and University of New South Wales‐Network Based 15 (UNSW‐NB15) datasets. The collected intrusion data are then normalized/scaled by employing a standard scaler technique. Next, the informative feature values are selected by employing the proposed optimization algorithm—that is, the Niche‐Strategy‐based Gorilla Troops Optimization (NSGTO) algorithm. Finally, these selected informative feature values are transferred to the Long Short‐Term Memory (LSTM) model to classify the types of intrusion attacks on both datasets. In comparison to the existing ID systems, the proposed ID system based on the NSGTO‐LSTM model obtains a classification accuracy of 99.98% and 99.90% on both datasets.