Intrusion Detection Using Machine Learning

Abstract: Therapidgrowthoftechnologiesnotonly formulates life easier but also exposes a lotof securityissues. With the advancement of theInternetoveryears,thenumberofattacks overtheInternethasbeenincreased.Intrusion Detection System (IDS )isoneofthesupportive layers applicable to information security. IDS provideasalubriousenvironmentforbusiness and keeps away from suspicious network activities. Recently, Machine Learning (ML) algorithmsareappliedinIDSinorderto identify and classify the security threats. This paper explores the comparative study of various ML algorithms used in IDS for several applicationssuchasfogcomputing,Internetof Things (IoT), big data, smart city, and 5G network. In addition, this work also aims for classifying the intrusions using ML algorithms like Linear Discriminant Analysis (LDA), Classification and RegressionTrees (CART)and Random Forest.