ANN Based Whale Sine Algorithm for Optimal Intrusion Detection

Abstract

IDS (Intrusion Detection Systems) is extensively used to secure and monitor the networks. An efficient feature selection approach always directly influences on the performance styles such as computational information and integrity. Two techniques are proposed in this study, in order to detect the malicious activities that exists in the network–Optimal Whale Sine Algorithm (OWSA) for feature selection and ANN Weighted Random Forest (AWRF) for classification. The present study improves the IDS which is evaluated by using the NSL-KDD dataset. These proposed model in the present study obtains better results in accordance with performance metrics such as precision, recall, f1-score and accuracy and there by increases the efficiency of IDS. The proposed model produces more attractive outcomes that exposes the performance of the algorithm in order to select the best features and perform optimal intrusion detection.