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A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller

College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China
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Future Internet 2018, 10(9), 83; https://doi.org/10.3390/fi10090083
Received: 27 July 2018 / Revised: 16 August 2018 / Accepted: 21 August 2018 / Published: 23 August 2018
A data center network is vulnerable to suffer from concealed low-rate distributed denial of service (L-DDoS) attacks because its data flow has the characteristics of data flow delay, diversity, and synchronization. Several studies have proposed addressing the detection of L-DDoS attacks, most of them are only detect L-DDoS attacks at a fixed rate. These methods cause low true positive and high false positive in detecting multi-rate L-DDoS attacks. Software defined network (SDN) is a new network architecture that can centrally control the network. We use an SDN controller to collect and analyze data packets entering the data center network and calculate the Renyi entropies base on IP of data packets, and then combine them with the hidden Markov model to get a probability model HMM-R to detect L-DDoS attacks at different rates. Compared with the four common attack detection algorithms (KNN, SVM, SOM, BP), HMM-R is superior to them in terms of the true positive rate, the false positive rate, and the adaptivity. View Full-Text
Keywords: L-DDoS attacks; SDN; data center network; adaptive detection; HMM-R L-DDoS attacks; SDN; data center network; adaptive detection; HMM-R
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MDPI and ACS Style

Wang, W.; Ke, X.; Wang, L. A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller. Future Internet 2018, 10, 83. https://doi.org/10.3390/fi10090083

AMA Style

Wang W, Ke X, Wang L. A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller. Future Internet. 2018; 10(9):83. https://doi.org/10.3390/fi10090083

Chicago/Turabian Style

Wang, Wentao, Xuan Ke, and Lingxia Wang. 2018. "A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller" Future Internet 10, no. 9: 83. https://doi.org/10.3390/fi10090083

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