Design and Development of an Efficient Network Intrusion Detection System Using Machine Learning Techniques

Abstract

Today’s internets are made up of nearly half a million different networks. In any network connection, identifying the attacks by their types is a difficult task as different attacks may have various connections, and their number may vary from a few to hundreds of network connections. To solve this problem, a novel hybrid network IDS called NID-Shield is proposed in the manuscript that classifies the dataset according to different attack types. Furthermore, the attack names found in attack types are classified individually helping considerably in predicting the vulnerability of individual attacks in various networks. The hybrid NID-Shield NIDS applies the efficient feature subset selection technique called CAPPER and distinct machine learning methods. The UNSW-NB15 and NSL-KDD datasets are utilized for the evaluation of metrics. Machine learning algorithms are applied for training the reduced accurate and highly merit feature subsets obtained from CAPPER and then assessed by the cross-validation method for the reduced attributes. Various performance metrics show that the hybrid NID-Shield NIDS applied with the CAPPER approach achieves a good accuracy rate and low FPR on the UNSW-NB15 and NSL-KDD datasets and shows good performance results when analyzed with various approaches found in existing literature studies.