<![CDATA[The Distributed Denial of Service (DDoS) attack is a type of cyberattack that aims to render a service, network, or website inaccessible to legitimate users. This attack not only disrupts services but also causes server crashes by repeatedly sending data packets, commonly referred to as spam. DDoS attacks can be identified as traffic anomalies. The National Cyber and Crypto Agency (BSSN) recorded 403,990,813 traffic anomalies with 347 cases specifically attributed to DDoS attacks. Based on this issue, a model capable of classifying DDoS attacks is necessary. This study employs the Random Forest and Support Vector Machine (SVM) methods through the steps of data collection, dataset loading, data preprocessing, classification modeling, and performance evaluation. In the final stage, the best method between Random Forest and Support Vector Machine is determined. The results indicate that Random Forest achieved an accuracy of 99.9%, whereas Support Vector Machine obtained an accuracy of 97.0%. Therefore, it can be concluded that Random Forest demonstrates better accuracy in classifying DDoS attacks.]]>
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