Network Attacks Anomaly Detection Using SNMP MIB Interface Parameters

Authors: Ghazi Al-Naymatm, Ahmed Hambouz, Mouhammd Alkasassbeh
Published: 2019-05-14 | Updated: 2019-10-19

Source: https://arxiv.org/abs/1906.00865

PDF: https://arxiv.org/pdf/1906.00865

Labels Predicted by AI

ネットワークパケット管理 MLベースのIDS データセット分析

Please note that these labels were automatically added by AI. Therefore, they may not be entirely accurate.
For more details, please see the About the Literature Database page.

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 minimal hardware resources consumption. Thus, this model can be adopted in intrusion detection system (IDS) to increase its performance and efficiency.