Detecting network anomalies using machine learning and SNMP-MIB dataset with IP group

Abstract

SNMP-MIB is a widely used approach that uses machine learning to classify data and obtain results, but using SNMP-MIB huge dataset is not efficient and it is also time and resources consuming. In this paper, a REP Tree, J48(Decision Tree) and Random Forest classifiers were used to train a model that can detect the anomalies and predict the network attacks that my affect the Internet Protocol(IP) group. This trained model can be used in the devices that are used to detect the anomalies such as intrusion detection systems.