  One of the most effective threats that targeting cybercriminals to limit
network performance is Denial of Service (DOS) attack. Thus, data security,
completeness and efficiency could be greatly damaged by this type of attacks.
This paper developed a network traffic system that relies on adopted dataset to
differentiate the DOS attacks from normal traffic. The detection model is built
with five Rule-based machine learning classifiers (DecisionTable, JRip, OneR,
PART and ZeroR). The findings have shown that the ICMP variables are
implemented in the identification of ICMP attack, HTTP flood attack, and
Slowloris at a high accuracy of approximately 99.7% using PART classifier. In
addition, PART classifier has succeeded in classifying normal traffic from
different DOS attacks at 100%.
