An improved Harris Hawks optimizer based feature selection technique with effective two-staged classifier for network intrusion detection system

Due to the increase in network attacks, maintaining network security is significantly difficult, to overcome security vulnerabilities Intrusion Detection System (IDS) is utilized. IDS is a software application that monitors the network traffic and detects the malicious activity in the network. Network Intrusion Detection System (NIDS) identifies the suspicious behaviour of nodes in the network by analysing the network traffic. Most of the existing IDS suffer from achieving better feature selection with high classification accuracy with reduced false alarm rate. In the proposed system, the Principal Component Analysis (PCA) technique is utilized to reduce the dimensionality of the dataset. Improved Harris Hawks Optimizer (IHHO) is employed for effective feature selection which provides powerful global search capability. For classification, two-staged classifier is proposed which employs Support Vector Machine (SVM) for stage-1 and K-Nearest Neighbors (KNN) for stage-2. The main goal of the proposed system is to combine the advantages of SVM and KNN to enhance classification accuracy with a reduced false alarm rate. The performance of the proposed system is evaluated by using the NSL- KDD dataset and it has achieved an overall classification accuracy of 95.01%, a False alarm rate of 0.01%, and an overall detection rate of 92.01%.