Although the prevention of Distributed Denial of Service (DDoS) attacks is not possible, detection of such attacks plays main
role in preventing their progress. In the flooding attacks, especially new sophisticated DDoS, the attacker floods the network
traffic toward the target computer by sending pseudo-normal packets. Therefore, multi-purpose IDSs do not offer a good performance
(and accuracy) in detecting such kinds of attacks.
In this paper, a novel method for detection of DDoS attacks has been introduced based on a statistical pre-processor and an
unsupervised artificial neural net. In addition, SPUNNID system has been designed based on the proposed method. The statistical
pre-processing has been used to extract some statistical features of the traffic, showing the behavior of DDoS attacks. The
unsupervised neural net is used to analyze and classify them as either a DDoS attack or normal. Moreover, the method has been
more investigated using attacked network traffic, which has been provided from a real environment. The experimental results
show that SPUNNID detects DDoS attacks accurately and efficiently.
Keywords DDoS Attacks - Intrusion Detection System - Unsupervised Neural Nets - Statistical Pre-Processor