Abstract
As one of the next generation of network architectures, Software-Defined Networking (SDN) decouples the forwarding and control function of the traditional network device. However, it also faces new threats from network attacks, such as the Low-rate Distributed Denial of Service (L-DDoS) attack. To resist L-DDoS attack in SDN, this work proposes METER, an enseMble discrEte wavelet Transform-based method for idEntifying low-Rate DDoS attack in SDN. The rationale for METER is to identify L-DDoS attacks based on a new metric referred to as the Ensemble Wavelet Energy Entropy set (EWEEs), which is calculated by a novel ensemble DWT method. Firstly, the ensemble wavelet coefficients matrix is attained by METER using the ensemble DWT method. After that, METER combines the related entropy values with wavelet energy of the ensemble wavelet coefficients matrix to obtain EWEEs. Furthermore, to increase the precision of the L-DDoS detection method, machine learning methods are used to mine the correlation between EWEEs and L-DDoS attacks for identifying L-DDoS. The experiments were implemented on the RYU controller and Mininet, which demonstrates that METER outperforms the baseline method, in terms of precision, accuracy, F -score, recall, ROC curve, and P-R curve.
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