Evaluating the Impact of Feature Selection Methods on SNMP-MIB Interface Parameters to Accurately Detect Network Anomalies

Many approaches have evolved to enhance the process of detecting network anomalies using SNMP-MIBs. Most of these approaches focus on machine learning algorithms with a lot of SNMP-MIB database parameters, which may consume most of the hardware resources (CPU, memory, and bandwidth). In this paper, we introduce an efficient detection model to detect network anomalies using Lazy. IBk as a machine learning classifier, Correlation, and ReliefF as an approach for attribute evaluators only SNMP-MIB interface parameters. This model achieves a high accuracy of 99.94% with minimal hardware resources consumption. Thus, this model can be adopted in the intrusion detection system (IDS) to increase its performance and efficiency.

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