DoS and DDoS Attack Detection in IoT Infrastructure using Xception Model with Explainability

Abstract

The denial of service (DoS) and distributed denial of service (DDoS) attacks are considered the most frequent attacks targeting the Internet of Things (IoT) network infrastructure globally. The current approaches for detecting DoS and DDoS attacks mainly use intrusion detection systems, traffic monitoring, and firewalls. However, complex DoS and DDoS attacks can bypass these detection mechanisms. Thus, this paper proposes utilizing convolutional neural network-based transfer learning to detect DoS and DDoS attacks from converted network traffic data into images. We employed the Xception model with fine-tuning, and we achieved
an average of 91% accuracy in detecting eleven different types of DoS and DDoS attacks, which is higher than the current state-of-the-art by 5% targeting the same task.