Automatic machine learning platform and method for network security application
The embodiment of the invention provides a network security application-oriented automatic machine learning platform and method. The platform comprises a data preprocessing module, a sample and label generation module, a feature extraction module and a model training module. The data preprocessing m...
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| creator | LIANG QUN CHEN GANG DENG QIAOHUA |
| description | The embodiment of the invention provides a network security application-oriented automatic machine learning platform and method. The platform comprises a data preprocessing module, a sample and label generation module, a feature extraction module and a model training module. The data preprocessing module is used for preprocessing the traffic data to obtain a behavior sequence corresponding to the user ID; the sample and label generation module is used for generating a sample and a label corresponding to the sample according to the behavior sequence corresponding to the user ID; the feature extraction module is used for performing feature extraction on the samples to obtain sample features; and the model training module is used for constructing an initial model by adopting a model structure corresponding to the data format of the sample features, and training the initial model by utilizing the sample features and the labels corresponding to the sample features to obtain a target model. In this way, user partic |
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The platform comprises a data preprocessing module, a sample and label generation module, a feature extraction module and a model training module. The data preprocessing module is used for preprocessing the traffic data to obtain a behavior sequence corresponding to the user ID; the sample and label generation module is used for generating a sample and a label corresponding to the sample according to the behavior sequence corresponding to the user ID; the feature extraction module is used for performing feature extraction on the samples to obtain sample features; and the model training module is used for constructing an initial model by adopting a model structure corresponding to the data format of the sample features, and training the initial model by utilizing the sample features and the labels corresponding to the sample features to obtain a target model. 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The platform comprises a data preprocessing module, a sample and label generation module, a feature extraction module and a model training module. The data preprocessing module is used for preprocessing the traffic data to obtain a behavior sequence corresponding to the user ID; the sample and label generation module is used for generating a sample and a label corresponding to the sample according to the behavior sequence corresponding to the user ID; the feature extraction module is used for performing feature extraction on the samples to obtain sample features; and the model training module is used for constructing an initial model by adopting a model structure corresponding to the data format of the sample features, and training the initial model by utilizing the sample features and the labels corresponding to the sample features to obtain a target model. In this way, user partic</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC COMMUNICATION TECHNIQUE</subject><subject>ELECTRICITY</subject><subject>PHYSICS</subject><subject>TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjDEOwjAMRbMwIOAO5gAMEYS9qkBMsLBXVurSqIkdJa4Qt6cDB2D6etJ7f20ezaySUIOHhH4MTBAJCwd-QY6og5QEyD0k0lF6WBiY9C1lgkp-LkE_gDnH4JcT4a1ZDRgr7X67Mfvr5dneDpSlo5rR05J37d3as3XOnWxz_Mf5AqIPN5I</recordid><startdate>20230523</startdate><enddate>20230523</enddate><creator>LIANG QUN</creator><creator>CHEN GANG</creator><creator>DENG QIAOHUA</creator><scope>EVB</scope></search><sort><creationdate>20230523</creationdate><title>Automatic machine learning platform and method for network security application</title><author>LIANG QUN ; CHEN GANG ; DENG QIAOHUA</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN116155541A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC COMMUNICATION TECHNIQUE</topic><topic>ELECTRICITY</topic><topic>PHYSICS</topic><topic>TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION</topic><toplevel>online_resources</toplevel><creatorcontrib>LIANG QUN</creatorcontrib><creatorcontrib>CHEN GANG</creatorcontrib><creatorcontrib>DENG QIAOHUA</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LIANG QUN</au><au>CHEN GANG</au><au>DENG QIAOHUA</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Automatic machine learning platform and method for network security application</title><date>2023-05-23</date><risdate>2023</risdate><abstract>The embodiment of the invention provides a network security application-oriented automatic machine learning platform and method. The platform comprises a data preprocessing module, a sample and label generation module, a feature extraction module and a model training module. The data preprocessing module is used for preprocessing the traffic data to obtain a behavior sequence corresponding to the user ID; the sample and label generation module is used for generating a sample and a label corresponding to the sample according to the behavior sequence corresponding to the user ID; the feature extraction module is used for performing feature extraction on the samples to obtain sample features; and the model training module is used for constructing an initial model by adopting a model structure corresponding to the data format of the sample features, and training the initial model by utilizing the sample features and the labels corresponding to the sample features to obtain a target model. In this way, user partic</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC COMMUNICATION TECHNIQUE ELECTRICITY PHYSICS TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION |
| title | Automatic machine learning platform and method for network security application |
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