A Data-Driven Approach for Classifying and Predicting DDoS Attacks with Machine Learning

Abstract

The importance of IoT security is growing as a result of the growing number of IoT devices and their many applications.  Distributed denial of service (DDoS) assaults on IoT systems have become more frequent, sophisticated, and of a different kind, according to recent research on network security, making DDoS one of the most formidable dangers. Real, lucrative, and efficient cybercrimes are carried out using DDoS attacks. One of the most dangerous types of assaults in network security is the DDoS attack. ML-based DDoS-detection systems continue to face obstacles that negatively impact their accuracy. AI, which incorporates ML to detect cyberattacks, is the most often utilized approach for these goals. In this study, it is suggested that DDoS assaults in Software-Defined Networking be identified and countered using ML approaches. The F1-score, recall, accuracy, and precision of many ML techniques, including Cat Boost and Extra Tree classifier, are compared in the suggested model. DDoS-Net is designed to handle data imbalance effectively and incorporates thorough feature analysis to enhance the model's detection capabilities. Evaluation on the UNSW-NB15 dataset demonstrates the exceptional performance of DDoS-Net. The highest accuracy achieved by the machine learning algorithms Cat Boost and Extra Tree classifier is 90.78% and 90.27% respectively using the most familiar dataset. This work presents a strong and precise approach for DDoS attack detection, which greatly improves the cybersecurity environment and strengthens digital infrastructures against these ubiquitous threats.