Distributed Denial of Service (DDoS) attacks became the most widely spread attack because of their ease of design and execution. Such attacks are challenging to detect and mitigate due to the diversity of DDoS attack modes and the variable size of attack traffic. This makes the research on DDoS attack detection extremely important. Machine learning techniques are used to detect various DDoS attacks with complex and dynamic patterns. However, such techniques require extensive pre-processing and feature engineering to the data to achieve acceptable results. On the other hand, neural networks can achieve acceptable results without the need for such prior preparations. This paper proposes a novel combination scheme between EfficientNet, Xception, and Decision Tree models called DTEXNet. DTEXNet combines two neural networks to benefit from their ability to extract features without the need to prior preparations, and a classical machine learning model that has high performance on similar problems. The solution proposed uses two convolutional neural networks (CNNs) to classify between 10 types of DDoS attacks and uses their prediction results to enhance the performance of Decision Tree model on the same classification task. The results of the experiments carried out show that the proposed solution can significantly improve the results of the Decision tree, EfficientNet, and Xception models if applied individually.
Article ID: 2022L30
Venue: Canadian Conference on Artificial Intelligence
Publisher: Canadian Artificial Intelligence Association