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Network Attacks Anomaly Detection Using SNMP MIB Interface Parameters
[article]
2019
arXiv
pre-print
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.
arXiv:1906.00865v2
fatcat:fy2zvezxszhbndjzlzdrbadgb4