Detecting DDoS Attacks in Network Traffic Based on Supervised Machine Learning Techniques

Abstract

One of the major concerns in network security that pose a big challenge to safeguarding networks is distributed denial-of-service (DDoS) attacks. Such attacks often lead to breaches of trust in online systems, cause significant losses in financial markets, and deny services to legitimate users. This study aims to propose a robust method for detecting DDOS attacks accurately. To accomplish this goal, the study investigated several machine learning algorithms in detecting such attacks utilizing the CIC-DDOS-2019 dataset, a well-known benchmark dataset characterized by its comprehensive coverage of DDOS attacks. Five machine learning algorithms have been evaluated: Random Forest (RF), Naive Bayes (NB), Logistic Regression (LR), J48 Decision Tree, and XGBoost based on their performance in detecting and discriminating between DDoS attacks and benign records. The results show high detection capability, with accuracy rates above 99% for all models except for NB. The RF, LR, J48, and XGBoost algorithms can recognize intricate DDoS assault patterns. In addition to comparing several machine learning methods for DDoS detection, this study provides insight into how these models can be helpful in real-world scenarios for improving network security.