부산대학교 도서관

Ensemble techniques could find DDoS attacks more quickly in IoT. A more dependable and precise detection system can be achieved by integrating the most advantageous attributes of XGBoost and Random Forest. This ensures that Internet of Things systems remain operational. Internet of Things (IoT) systems are significantly compromised regarding security and dependability by Distributed Denial of Service (DDoS) assaults. Ensemble approaches, which utilize multiple machine learning models to generate predictions, appear to be a viable method for facilitating the detection of DDoS attacks in such contexts. Conventional defenses against these everevolving attacks prove inadequate when applied to a singular method. It emphasizes Random Forest and XGBoost. The accuracy of the proposed ensemble in detecting distributed denial of service attacks initiated by Internet of Things devices is 99%. The findings demonstrate how ensemble methodologies can enhance the precision and resilience of DDoS attack detection in Internet of Things systems against adversarial assaults.