Anomaly based DDoS Attack Detection

Abstract

Distributed denial-of-service (DDoS) attack poses a serious threat to network security. Several methods have been introduced to reduce the damage. However, most of the methods have been found unable to detect the attack in real-time with high detection accuracy. This paper presents a simple yet effective method to detect DDoS attack for all possible attack scenarios given by Mirkoviac [1] viz constant rate, pulsing rate, increasing rate and sub-group. The proposed method is validated using well known CAIDA dataset.