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

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