Anomaly based DDoS Attack Detection

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 realtime 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.