Intrusion detection framework using stacked auto encoder based deep neural network in IOT network

Abstract

Security is of paramount importance in the number of systems affiliated with increased IoT. Therefore, in this manuscript, a Stacked Auto Encoder based Deep Neural Network (DNN) fostered Intrusion Detection Framework is proposed to secure the IoT Environment. Here, the data is given to the preprocessing stage, in which redundancy elimination and replacement of missing value are done. Then, the preprocessed output is given to the feature selection process. Wherein, the Golden eagle optimization (GEO) algorithm selects the optimum features from pre‐processed data sets. Then selected features are given to the Stacked Auto encoder based deep neural network for classification, which classified the data, like normal, anomalies. Here, the proposed approach is implemented in Python language. To check the robustness of the proposed approach, the performance metrics, like accuracy, specificity, sensitivity, F‐measure, precision, and recall is measured. The simulation outcome show that the proposed Stacked Auto Encoder based Deep Neural Network based Intrusion Detection Framework (IDS‐FS‐GEO‐SAENN) method attains higher accuracy 99.75%, 97.85%, 95.13%, and 98.79, higher sensitivity 96.34%, 91.23%, 89.12%, and 87.25%, higher specificity 93.67%, 92.37%, 98.47%, and 94.78% compared with the existing methods, like FS‐SMO‐SDPN, FS‐WO‐RNNLSTM, FS‐hybrid GWOPSO‐RF, and FS‐CNNLSTMGRU, respectively.