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Deep learning based DDOS defensive device and method in SDN

A deep learning and DDOS technology, applied in the field of network communication, can solve problems such as difficulty in defense and ferocious attacks, and achieve the effects of improving accuracy, improving high cohesion characteristics, and reducing coupling correlation

Active Publication Date: 2017-03-22
ZHEJIANG GONGSHANG UNIVERSITY
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AI Technical Summary

Problems solved by technology

Since DDoS is different from a DoS attack that only needs a computer terminal and a modem, a DDoS attack uses a group of controlled machines to launch an attack on a fixed site at the same time. Such attacks are fierce and difficult to guard against. more destructive

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  • Deep learning based DDOS defensive device and method in SDN
  • Deep learning based DDOS defensive device and method in SDN
  • Deep learning based DDOS defensive device and method in SDN

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Embodiment Construction

[0049] At present, most SDN architectures use the OpenFlow protocol as the communication interface between the control layer and the data exchange layer, which can change network resources and add new switch rules in real time. The OpenFlow protocol relies on the secure channel between the controller and the switch. Once a DDOS attack occurs on the secure channel and the connection is disconnected, the SDN architecture will collapse due to the loss of the controller. If a DDOS attack occurs between the switch and the controller, If the connection is lost, it means that the entire network architecture loses the control layer. In this case, the entire network may be paralyzed. To sum up, it is imminent to study an efficient and accurate DDoS security defense measure. The present invention provides a DDOS defense device and method based on deep learning in SDN, which is mainly used to solve the problem of how to effectively defend against DDOS attacks in an SDN network. The pres...

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Abstract

The invention discloses a deep learning based DDOS defensive device and method in SDN. The device comprises a feature extraction module, a deep learning DDOS detection module, a Model Updater module, an information statistics module and a flow table generation module. The feature extraction module extracts features of an input data packet in the system to construct a feature matrix, processed features are input to the deep learning DDOS detection module, and the deep learning DDOS detection module uses a learned model to determine whether the input data packet in the present system is an attack packet. According to the invention, deep learning is used to detect the data packet input to the system, and compared with a traditional DDOS attack invasion detection method, the detection efficiency and the accuracy are improved greatly.

Description

technical field [0001] The invention relates to the technical field of network communication, in particular to a DDOS defense device and method based on deep learning in SDN. Background technique [0002] With the rapid development of the global informatization process, attackers in the network take advantage of the security loopholes in the system architecture of the network and the server system in the network, or steal personal information of network users, or destroy the normal network environment, or prevent the target host from Normal interactive communication, the network environment is facing increasingly serious security challenges. With the explosive growth of the number of Internet users in recent years, new network applications, such as social networking, high-definition online video, and innovative service models, such as cloud computing and big data, have brought new challenges to traditional networks. The development of the traditional network architecture in...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/06
CPCH04L63/1416H04L63/1458
Inventor 李传煌孙正君龚梁金蓉王伟明
Owner ZHEJIANG GONGSHANG UNIVERSITY
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