This document discusses a statistical approach for classifying and identifying different types of Distributed Denial of Service (DDoS) attacks using the UCLA dataset. It first introduces DDoS attacks and their increasing prevalence. It then discusses related work on DDoS attack detection. The document outlines the architecture of DDoS attacks and describes some common types like SYN flooding and ACK flooding attacks. The proposed system is described which involves collecting packets, extracting features, using a packet classification algorithm to initially classify attacks, then using a K-Nearest Neighbors classifier for more accurate results. Finally, the system aims to classify and identify specific types of DDoS attacks from the network traffic analysis.