Distributed Denial of Service (DDOS) Attack Detection Using Classification Algorithm
S Haribalaji, P. Ranjana · 2024
Strong and flexible detection systems are vital to protect network infrastructures from Distributed Denial of Service (DDoS) attacks, which are becoming more common and sophisticated. To detect DDoS attacks, this study investigates using three different classification algorithms: Random Forest, Logistic Regression, and K-Nearest Neighbor (KNN).The research uses labeled datasets gathered using network monitoring tools and includes both normal and DDoS attack traffic. The KNN Classifier, Random Forest, and Logistic Regression are the methods used to train and test the attack detection model. The outcomes of these methods must be compared and visualized to identify the model with the highest accuracy rate. To increase accuracy, a modification is made to the K nearest neighbor algorithm.