Out-of-distribution network flow data detection method based on calculated likelihood ratio
The invention discloses an out-of-distribution network flow data detection method based on a calculated likelihood ratio, and belongs to the field of network flow data detection. The objective of the invention is to improve the accuracy and confidence of network flow data identification. The method...
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| creator | ZHAO YUE LIU FAN SHI KAIYU CHE JIAZHEN ZHANG XIAOHUI FENG SHUAI SHI JIANTAO MIAO JUNZHONG WEI XIANKUI LIU LIKUN SONG YUNZU GE MENGMENG LI JINGWEI WANG JIUJIN GUO MINGHAO YU XIANGZHAN YE LIN |
| description | The invention discloses an out-of-distribution network flow data detection method based on a calculated likelihood ratio, and belongs to the field of network flow data detection. The objective of the invention is to improve the accuracy and confidence of network flow data identification. The method comprises the following steps: extracting network traffic characteristics: the original traffic is a pcap packet, is divided into different data streams according to a quintuple, and is set to extract a data packet length sequence, calculate a packet arrival time interval sequence, store the sequences and generate a CSV file as original training data of model training; the method comprises the following steps: training an original classification model by using original training data, training the original classification model by adopting a deep learning algorithm long-short-term memory network to obtain a model trained by the original training data, generating disturbance data, generating disturbance data by adopti |
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The objective of the invention is to improve the accuracy and confidence of network flow data identification. The method comprises the following steps: extracting network traffic characteristics: the original traffic is a pcap packet, is divided into different data streams according to a quintuple, and is set to extract a data packet length sequence, calculate a packet arrival time interval sequence, store the sequences and generate a CSV file as original training data of model training; the method comprises the following steps: training an original classification model by using original training data, training the original classification model by adopting a deep learning algorithm long-short-term memory network to obtain a model trained by the original training data, generating disturbance data, generating disturbance data by adopti</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC COMMUNICATION TECHNIQUE ; ELECTRICITY ; PHYSICS ; TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION</subject><creationdate>2022</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220802&DB=EPODOC&CC=CN&NR=114844840A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220802&DB=EPODOC&CC=CN&NR=114844840A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>ZHAO YUE</creatorcontrib><creatorcontrib>LIU FAN</creatorcontrib><creatorcontrib>SHI KAIYU</creatorcontrib><creatorcontrib>CHE JIAZHEN</creatorcontrib><creatorcontrib>ZHANG XIAOHUI</creatorcontrib><creatorcontrib>FENG SHUAI</creatorcontrib><creatorcontrib>SHI JIANTAO</creatorcontrib><creatorcontrib>MIAO JUNZHONG</creatorcontrib><creatorcontrib>WEI XIANKUI</creatorcontrib><creatorcontrib>LIU LIKUN</creatorcontrib><creatorcontrib>SONG YUNZU</creatorcontrib><creatorcontrib>GE MENGMENG</creatorcontrib><creatorcontrib>LI JINGWEI</creatorcontrib><creatorcontrib>WANG JIUJIN</creatorcontrib><creatorcontrib>GUO MINGHAO</creatorcontrib><creatorcontrib>YU XIANGZHAN</creatorcontrib><creatorcontrib>YE LIN</creatorcontrib><title>Out-of-distribution network flow data detection method based on calculated likelihood ratio</title><description>The invention discloses an out-of-distribution network flow data detection method based on a calculated likelihood ratio, and belongs to the field of network flow data detection. The objective of the invention is to improve the accuracy and confidence of network flow data identification. 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The objective of the invention is to improve the accuracy and confidence of network flow data identification. The method comprises the following steps: extracting network traffic characteristics: the original traffic is a pcap packet, is divided into different data streams according to a quintuple, and is set to extract a data packet length sequence, calculate a packet arrival time interval sequence, store the sequences and generate a CSV file as original training data of model training; the method comprises the following steps: training an original classification model by using original training data, training the original classification model by adopting a deep learning algorithm long-short-term memory network to obtain a model trained by the original training data, generating disturbance data, generating disturbance data by adopti</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 | Out-of-distribution network flow data detection method based on calculated likelihood ratio |
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