چکیده (انگلیسی):

Distributed Denial of Service attacks (DDoS) overwhelm network resources with useless or harmful packets
and prevent normal users from accessing these network resources. These attacks jeopardize the
confidentiality, privacy and integrity of information on the internet. Since it is very difficult to set any
predefined rules to correctly identify genuine network traffic, an anomaly-based Intrusion Detection
System (IDS) for network security is commonly used to detect and prevent new DDoS attacks. Data
mining methods can be used in intrusion detection systems, such as clustering k-means, artificial
neural network. Since the clustering methods can be used to aggregate similar objects, they can detect
DDoS attacks to reduce false-positive rates. In this study, a review of DDoS attacks using clustering
data mining techniques is presented. A review illustrates the most recent, state-of-the art science for
clustering techniques to detect DDoS attacks.