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The possibility of programming the control and data planes, enabled by theSoftware-Defined Networking (SDN) paradigm, represents a fertile ground on topof which novel operation and management mechanisms can be fully explored, beingDistributed Denial of Service (DDoS) attack detection based on machine learningtechniques the focus of this work. To carry out the detection, this paperproposes ORACLE: cOllaboRation of dAta and Control pLanEs to detect DDoSattacks, an architecture that promotes the coordination of control and dataplanes to detect network attacks. As its first contribution, this architecturedelegates to the data plane the extraction and processing of trafficinformation collected per flow. This is done in order to ease the calculationand classification of the feature set used in the attack detection, as theneeded flow information is already processed when it arrives at the controlplane. Besides, as the second contribution, this architecture breaks thelimitations to calculate some features that are not possible to implement in atraditional OpenFlow-based environment. In the evaluation of ORACLE, weobtained up to 96% of accuracy in the testing phase, using a K-Nearest Neighbormodel.

ORACLE: Collaboration of Data and Control Planes to Detect DDoS Attacks