Network Attacks Anomaly Detection Using SNMP MIB Interface Parameters

TL;DR

This paper presents an efficient network attack anomaly detection model using SNMP-MIB parameters, employing Lazy.IBk with attribute evaluators, achieving high accuracy with minimal resource consumption.

Contribution

Introduces a resource-efficient detection model using Lazy.IBk and attribute evaluators on SNMP-MIB data for network attack detection.

Findings

Achieved 100% detection accuracy

Minimal hardware resource consumption

Suitable for intrusion detection systems

Abstract

Many approaches have evolved to enhance network attacks detection anomaly 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 hardware resources (CPU, memory, and bandwidth). In this paper we introduce an efficient detection model to detect network attacks anomaly using Lazy.IBk as a machine learning classifier and Correlation, and ReliefF as attribute evaluators on SNMP-MIB interface parameters. This model achieved accurate results (100%) with minimal hardware resources consumption. Thus, this model can be adopted in intrusion detection system (IDS) to increase its performance and efficiency.