Abstract:
In the current fact of data center networking, a software-defined data center network (SDDN) has emerged as
a transformational solution to address the inherent complexities in network control. Nonetheless, even with so many
advantages to look up to, there are critically important issues making its implementation critical, where security,
performance, reliability, and fault tolerance are important. For this reason, security becomes a very vital issue, since
SDDNs are exposed to many Distributed Denial of Service (DDoS) attacks. In this regard, a new machine-learningbased
CURE algorithm framework has been proposed in this paper to outweigh the security challenges. It uses an
Adaptive CURE algorithm to minimize the effect of DDoS. The algorithm is designed with adaptive input, depending
on the processing resources. The controller captures the suspicious traffic acting as a central coordinator and, if an
anomaly in traffic is detected, then the same reforwards a copy of suspicious traffic to the processing and analyzing
unit. The adopted approach applies the Adaptive CURE algorithm in processing, through a comprehensive study of the
pattern of traffic, the anomalous traffic in the distinguishing of potential DDoS attacks with great accuracy. The
algorithm's intelligence facilitates the identification of DDoS attacks. This allows to update switches with suitable flow
entries by the controller. Such response mechanisms further improve the security posture of SDDN networks,
specifically providing a really strong defense against DDoS attacks. The experiment results show that the proposed
framework achieves an accuracy of up to 96.2% with various DDoS attacks.
