A Detection Approach for IoT Traffic-Based DDoS Attacks

The Internet of Things (IoT) is a rapidly growing technology that significantly changed the human life by automating everything around us. It enables us to manage IoT devices 24/7 from anywhere. However, it also brings several cyber security risks, such as Distributed Denial of Service (DDoS) attacks. A large-scale DDoS attack immediately overwhelms the victim’s network or system with a massive volume of unwanted traffic from the pool of compromised IoT devices. As a result, it prevents legitimate users from accessing the victim’s services or applications. Further, protecting Internet-based applications and networks from large-scale IoT traffic-based DDoS attacks is a challenging task. In the literature, several techniques are available to protect networks and services from IoT traffic-based DDoS attacks. However, the occurrence and sophistication of IoT traffic-based DDoS attacks are expanding every year. In this article, we propose a comprehensive approach for identifying IoT traffic-based DDoS attacks. The experimental results demonstrate that the proposed XGB-based model achieves significant accuracy of 99.89%.