Title:
TRAINING METHOD AND DETECTION METHOD FOR NETWORK TRAFFIC ANOMALY DETECTION MODEL
Document Type and Number:
WIPO Patent Application WO/2021/114231
Kind Code:
A1
Abstract:
Disclosed in the present invention is a training method and a detection method for a network traffic anomaly detection model. The network traffic anomaly detection model comprises a feature extraction network and a classification network. The training method comprises: determining, according to a training sample, the number of hidden layers and the number of neurons in each hidden layer; constructing an initial feature extraction network according to the number of hidden layers and the number of neurons in each hidden layer; using the training sample to train the initial feature extraction network, so as to obtain a trained feature extraction network; and extracting abstract feature data of the training sample by using the trained feature extraction network, and training a classification network by using the abstract feature data, so as to complete the training of a network traffic detection model. The network structure of the present application can adapt to network traffic data, and avoids the structure of the detection model being too complicated and too simple, thereby reducing generalization errors, and significantly reducing detection time and improving detection accuracy.
Inventors:
YE KEJIANG (CN)
JI SHUJIAN (CN)
XU CHENGZHONG (CN)
JI SHUJIAN (CN)
XU CHENGZHONG (CN)
Application Number:
PCT/CN2019/125189
Publication Date:
June 17, 2021
Filing Date:
December 13, 2019
Export Citation:
Assignee:
SHENZHEN INST OF ADV TECH CAS (CN)
International Classes:
H04L29/06; G06K9/62; G06N3/04; G06N3/08; H04L12/24
Foreign References:
| CN107725283A | 2018-02-23 | |||
| CN101686235A | 2010-03-31 | |||
| CN107959675A | 2018-04-24 | |||
| CN109728939A | 2019-05-07 | |||
| US20180351823A1 | 2018-12-06 | |||
| US20190036952A1 | 2019-01-31 |
Other References:
"Advances in Intelligent Data Analysis XIX", vol. 32, 29 September 2019, SPRINGER INTERNATIONAL PUBLISHING, Cham, ISBN: 978-3-030-71592-2, ISSN: 0302-9743, article JI SHUJIAN; SUN TONGZHENG; YE KEJIANG; WANG WENBO; XU CHENG-ZHONG: "DAFL: Deep Adaptive Feature Learning for Network Anomaly Detection", pages: 350 - 354, XP047524227, DOI: 10.1007/978-3-030-30709-7_32
Attorney, Agent or Firm:
MING & YUE INTELLECTUAL PROPERTY LAW FIRM (CN)
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