<![CDATA[Distributed Deniel of Service (DDoS) is one of the most popular attacks today. DDoS is what aims to crash the server system by flooding packets or requests on the network. Characteristics of Distributed Denial of Service (DDoS) attacks are difficult to distinguish from normal network traffic flows, so to identify these attacks, a system that can classify DDoS attacks is needed. In this study, a DDoS attack classification system has been built using the nave Bayes method. The dataset used is a dataset from CICIDS2018 which has 84 features that can help nave Bayes performance in classifying DDoS attacks. As a test sample, the test data is obtained from the results of the attack test using the Slowloris program and then the traffic flow is captured in real time using TCPdump. The capture results are extracted and converted into .csv extensions using the CICFlowMeter tool. Then the data will be preprocessed to eliminate empty data and select the most relevant features to facilitate the performance of the nave Bayes method in classifying. The level of accuracy of the classification results is calculated using a confusion matrix. Based on the test results, the researchers found that the proposed method can classify DDoS attacks with an accuracy rate of up to 95%.]]>
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