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## User: mouhammd-alkasassbeh

### Title: Network Attacks Anomaly Detection Using SNMP MIB Interface Parameters

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.

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