<![CDATA[Rapid developments in information technology have increased dependence on network services, but have also triggered an increase in cyber threats such as Distributed Denial of Service (DDoS). These attacks can paralyze systems by flooding servers with simultaneous fake traffic. Conventional rule-based detection methods are now less effective in dealing with dynamic attack patterns, requiring an adaptive approach based on machine learning. This research develops a Support Vector Machine (SVM) model enhanced with Blockchain technology to improve accuracy and data security in detecting DDoS attacks. The dataset used is CICDDoS2023 from the Canadian Institute for Cybersecurity, which contains various variants of modern DDoS attacks. The research stages include data pre-processing, training the SVM model using the RBF kernel, and integrating Blockchain with training data hash recording through a smart contract using Remix Ethereum to ensure data integrity. Performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics based on the confusion matrix results. The integration of SVM and Blockchain showed an increase in security and detection accuracy compared to conventional SVM models. This approach not only improves the reliability of the DDoS attack detection system, but also creates a transparent and tamper-proof data validation mechanism. The research results are expected to contribute to the development of adaptive, decentralized network security systems with a high level of confidence in attack detection results.]]>
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