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Detection and defense of DDoS attack–based on deep learning in OpenFlow ‐based SDN
Author(s) -
Li Chuanhuang,
Wu Yan,
Yuan Xiaoyong,
Sun Zhengjun,
Wang Weiming,
Li Xiaolin,
Gong Liang
Publication year - 2018
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.3497
Subject(s) - denial of service attack , computer science , openflow , application layer ddos attack , trace (psycholinguistics) , software defined networking , trinoo , computer network , software , computer security , distributed computing , the internet , operating system , linguistics , philosophy
Summary Distributed denial of service (DDoS) is a special form of denial of service attack. In this paper, a DDoS detection model and defense system based on deep learning in Software‐Defined Network (SDN) environment are introduced. The model can learn patterns from sequences of network traffic and trace network attack activities in a historical manner. By using the defense system based on the model, the DDoS attack traffic can be effectively cleaned in Software‐Defined Network. The experimental results demonstrate the much better performance of our model compared with conventional machine learning ways. It also reduces the degree of dependence on environment, simplifies the real‐time update of detection system, and decreases the difficulty of upgrading or changing detection strategy.