Adel Assiri*
CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 767-778, 2021, DOI:10.32604/cmc.2020.013813
Abstract Anomaly classification based on network traffic features is an important
task to monitor and detect network intrusion attacks. Network-based intrusion
detection systems (NIDSs) using machine learning (ML) methods are effective
tools for protecting network infrastructures and services from unpredictable and
unseen attacks. Among several ML methods, random forest (RF) is a robust method
that can be used in ML-based network intrusion detection solutions. However, the
minimum number of instances for each split and the number of trees in the forest
are two key parameters of RF that can affect classification accuracy. Therefore, optimal parameter selection is a real problem in… More >
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