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

TL;DR

This paper presents a machine learning approach using decision tree and random forest classifiers to efficiently detect network anomalies and predict attacks based on SNMP-MIB datasets, aiming to improve intrusion detection systems.

Contribution

It introduces a model trained with REP Tree, J48, and Random Forest classifiers specifically for anomaly detection in IP groups, addressing dataset size and efficiency issues.

Findings

Random Forest achieved high accuracy in anomaly detection

Decision trees provided fast classification performance

The model can be integrated into intrusion detection systems

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.