DDoS Attack Detection- Using AIML Techniques

Distributed Denial of Service (DDoS) attacks have emerged as a major threat to network security, causing significant disruptions to online services and substantial financial losses for organizations. This research investigates the applications of various machine learning techniques for DDoS attacks detection, utilizing the CICDDoS2019 dataset, which involves a wide spectrum of attack types and standard traffic patterns. We evaluate three prominent machine learning algorithms: Random Forest, XGBoost, and LightGBM. Our findings indicate that these models can effectively classify DDoS traffic with high accuracy, although performance varies across different attack types. The study emphasizes the significance of feature selection, data preprocessing, and model evaluation in enhancing detection capabilities. This research contributes to ongoing efforts to improve cyber security measures against evolving DDoS threats.