Deteksi Serangan Low Rate DDoS di SD-IoT Menggunakan Ensemble Boosting dengan Decision Tree dan AdaBoost

IoT devices are not equipped with user interfaces, computational capabilities, or storage facilities that are essential for implementing firewalls and other diagnostic tools. One approach to addressing the challenges in IoT networks is by integrating them with the Software Defined Network (SDN) architecture. The combination of SDN and IoT is referred to as SD-IoT. However, the centralized control system in SDN faces a major threat, namely Distributed Denial of Service (DDoS) attacks. LRDDoS attacks are more difficult to detect because they are hidden within normal-looking data flows. As a result, LRDDoS has become a major concern in SDN security, necessitating the development of a system capable of identifying and detecting such attacks. In this study, the author proposes an LRDDoS detection system using Ensemble Boosting with Decision Tree and AdaBoost. The dataset used for testing was obtained from previous research available through Mendeley Data. The classification results using Ensemble Boosting with Decision Tree and AdaBoost demonstrate strong performance across different packet rates per second (pps).