A Machine Learning Based DDoS Attack Detection Method In SDN Networks
Keywords: Software Defined Networking, DDoS attack, Machine learning, Network attack protection, SYN Flood
Abstract: The concept of Software Defined Networking (SDN)
represents a modern way to organize computer network as it
decouples the control plane from the data plane through network
abstraction. However, countering Distributed Denial-of-Service
(DDoS) attacks aimed at controllers has become a major issue in
SDNs, as the controller responsible for managing network traffic
is a sensitive failure point in the entire network architecture.
This article mainly introduces a method for extracting traffic
packet features in SDN Networks and utilizing machine learning
algorithm for their classification. This technology can be used to
identify packets in SDNs that are utilized for conducting DDoS
attacks to the network and protect the network from failing. In
our testing on a simple SDN Network using KDD-CPU99 dataset,
this method demonstrated acceptable performance.
Submission Number: 7
Loading