MLADAD: Machine Learning Algorithms Analysis on DDoS Attack Detection in SDN-IoT Networks
Vidya Sagar Thalapala, A. Ramamohana Reddy, Koppala Guravaiah · Procedia Computer Science · 2025
Now a days, increase in applications of SDN-IoT networks for their effective management of network capabilities. Controller is the brain of the SDN-IoT networks and have the capabilities of managing and controlling the entire network. Due this feature controller become the main threat to the attackers and become the main vulnerable point is SDN. If an attacker is able to gain control of the controller, then the attacker may be able to control the whole SDN-IoT network. Its required to protect the controller and entire network from the attacker. In this regard, DDoS attack detection is considered in SDN-IoT networks is necessary. Machine learning algorithms such as Logistic regression, Decision Tree, and Random Forest with the intrusion dataset CSE-CIC-IDS2018 has been used. The results suggest that Random Forest and Decision Tree has best accuracy with respect to detection rate.