A novel CNN-based approach for detection and classification of DDoS attacks

Among the recent network security issues, Distributed Denial of Service (DDoS) attack is one of the most dangerous threats in today's cyberspace that can disrupt essential services. These attacks compromise network security by flooding the target with malicious traffic. Several researchers have designed effective DDoS detection mechanisms using machine learning (ML) and deep learning (DL)-based techniques. However, existing detection approaches paid less attention to issues such as class imbalance, multi-classification, or computational cost of the models, especially time. In this study, we propose a novel framework for detecting and classifying DDoS attacks with high accuracy and low computational cost. To address the class imbalance, we employ random sampling, while feature selection techniques such as low information gain, quasi-constant elimination, and principal component analysis are utilized for optimal feature selection and reduction. Our proposed CNN-based model achieves outstanding performance, boasting an accuracy of 99.99% for binary classification and 98.44% for multi-classification. The proposed model is compared to existing works and baseline line models and found to be effective in binary and multi-classification.