<![CDATA[DDoS is a computer attack that is still trend for attackers. This attack is the most effective way to block the victim's resources by sending a large number of packets. Over time, DDoS attacks have grown and varied. Therefore, a DDoS attack detection that is adaptive to various types of attacks is needed. Machine learning-based IDS is a detection system that classify data using a specific algorithm. This study classified DDoS attacks use the C5.0 algorithm. This method can treat continuous variables and attribute selection based on the highest information gain as the parent for the next node. C5.0 has an additional function, namely boosting to improve accuracy. The dataset used is CICDDo2019. This data was developed by University of New Brunswick in 2019. The data used were 56279 instances including 25133 DDoS attacks and 31146 instances of normal network traffic. The results of testing the accuracy, precision, and recall evaluation of the C5.0 algorithm are 98.38%, 98.39%, and 98.37% and the required running time is 16.84 seconds.]]>
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