Cross-site scripting attack detection method and system based on deep learning
The invention discloses a cross-site scripting attack detection method and system based on deep learning. The method comprises the following steps: S1, acquiring XSS sample data in multiple channels; s2, performing sample construction based on the acquired XSS sample data and normal sample data, and...
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , |
|---|---|
| Format: | Patent |
| Sprache: | chi ; eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | LI ZIYUAN MA ZHICHENG DANG QIAN ZHANG LEI ZHENG GUANGYUAN ZHANG XUN BAI WANRONG WANG DI WANG BAOHUI WEI FENG ZHAO JINXIONG |
| description | The invention discloses a cross-site scripting attack detection method and system based on deep learning. The method comprises the following steps: S1, acquiring XSS sample data in multiple channels; s2, performing sample construction based on the acquired XSS sample data and normal sample data, and preprocessing the constructed sample to generate a sample data set; s3, based on a deep learning method, establishing an improved model which connects a CNN model and a BiLSTM model in series and combines an attention mechanism, and extracting sample data set features; s4, utilizing a feature vector obtained by carrying out transfer learning through BERT to accelerate convergence of the model, and improving the detection efficiency; and S5, deploying an improved model which connects the CNN model and the BiLSTM model in series and combines an attention mechanism, collecting data, and carrying out cross-site scripting attack detection. According to the scheme, the accuracy rate, the recall rate and the precision ra |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN116488898A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN116488898A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN116488898A3</originalsourceid><addsrcrecordid>eNqNizEOwjAQBN1QoMAfjgekiEDIlMgiokpFHx32AhaJbfmu4fek4AFUU8zM2gyuZpFWooLE11g0piexKvs3BSi8xpxohr5yIE6B5COKme4sCLSoABSawDUt58asHjwJtj82Ztdfbu7aouQRUtgjQUc3dN3xYK092fP-n-YLJ6A2uA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Cross-site scripting attack detection method and system based on deep learning</title><source>esp@cenet</source><creator>LI ZIYUAN ; MA ZHICHENG ; DANG QIAN ; ZHANG LEI ; ZHENG GUANGYUAN ; ZHANG XUN ; BAI WANRONG ; WANG DI ; WANG BAOHUI ; WEI FENG ; ZHAO JINXIONG</creator><creatorcontrib>LI ZIYUAN ; MA ZHICHENG ; DANG QIAN ; ZHANG LEI ; ZHENG GUANGYUAN ; ZHANG XUN ; BAI WANRONG ; WANG DI ; WANG BAOHUI ; WEI FENG ; ZHAO JINXIONG</creatorcontrib><description>The invention discloses a cross-site scripting attack detection method and system based on deep learning. The method comprises the following steps: S1, acquiring XSS sample data in multiple channels; s2, performing sample construction based on the acquired XSS sample data and normal sample data, and preprocessing the constructed sample to generate a sample data set; s3, based on a deep learning method, establishing an improved model which connects a CNN model and a BiLSTM model in series and combines an attention mechanism, and extracting sample data set features; s4, utilizing a feature vector obtained by carrying out transfer learning through BERT to accelerate convergence of the model, and improving the detection efficiency; and S5, deploying an improved model which connects the CNN model and the BiLSTM model in series and combines an attention mechanism, collecting data, and carrying out cross-site scripting attack detection. According to the scheme, the accuracy rate, the recall rate and the precision ra</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>2023</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=20230725&DB=EPODOC&CC=CN&NR=116488898A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76516</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230725&DB=EPODOC&CC=CN&NR=116488898A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LI ZIYUAN</creatorcontrib><creatorcontrib>MA ZHICHENG</creatorcontrib><creatorcontrib>DANG QIAN</creatorcontrib><creatorcontrib>ZHANG LEI</creatorcontrib><creatorcontrib>ZHENG GUANGYUAN</creatorcontrib><creatorcontrib>ZHANG XUN</creatorcontrib><creatorcontrib>BAI WANRONG</creatorcontrib><creatorcontrib>WANG DI</creatorcontrib><creatorcontrib>WANG BAOHUI</creatorcontrib><creatorcontrib>WEI FENG</creatorcontrib><creatorcontrib>ZHAO JINXIONG</creatorcontrib><title>Cross-site scripting attack detection method and system based on deep learning</title><description>The invention discloses a cross-site scripting attack detection method and system based on deep learning. The method comprises the following steps: S1, acquiring XSS sample data in multiple channels; s2, performing sample construction based on the acquired XSS sample data and normal sample data, and preprocessing the constructed sample to generate a sample data set; s3, based on a deep learning method, establishing an improved model which connects a CNN model and a BiLSTM model in series and combines an attention mechanism, and extracting sample data set features; s4, utilizing a feature vector obtained by carrying out transfer learning through BERT to accelerate convergence of the model, and improving the detection efficiency; and S5, deploying an improved model which connects the CNN model and the BiLSTM model in series and combines an attention mechanism, collecting data, and carrying out cross-site scripting attack detection. According to the scheme, the accuracy rate, the recall rate and the precision ra</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>eNqNizEOwjAQBN1QoMAfjgekiEDIlMgiokpFHx32AhaJbfmu4fek4AFUU8zM2gyuZpFWooLE11g0piexKvs3BSi8xpxohr5yIE6B5COKme4sCLSoABSawDUt58asHjwJtj82Ztdfbu7aouQRUtgjQUc3dN3xYK092fP-n-YLJ6A2uA</recordid><startdate>20230725</startdate><enddate>20230725</enddate><creator>LI ZIYUAN</creator><creator>MA ZHICHENG</creator><creator>DANG QIAN</creator><creator>ZHANG LEI</creator><creator>ZHENG GUANGYUAN</creator><creator>ZHANG XUN</creator><creator>BAI WANRONG</creator><creator>WANG DI</creator><creator>WANG BAOHUI</creator><creator>WEI FENG</creator><creator>ZHAO JINXIONG</creator><scope>EVB</scope></search><sort><creationdate>20230725</creationdate><title>Cross-site scripting attack detection method and system based on deep learning</title><author>LI ZIYUAN ; MA ZHICHENG ; DANG QIAN ; ZHANG LEI ; ZHENG GUANGYUAN ; ZHANG XUN ; BAI WANRONG ; WANG DI ; WANG BAOHUI ; WEI FENG ; ZHAO JINXIONG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN116488898A3</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>LI ZIYUAN</creatorcontrib><creatorcontrib>MA ZHICHENG</creatorcontrib><creatorcontrib>DANG QIAN</creatorcontrib><creatorcontrib>ZHANG LEI</creatorcontrib><creatorcontrib>ZHENG GUANGYUAN</creatorcontrib><creatorcontrib>ZHANG XUN</creatorcontrib><creatorcontrib>BAI WANRONG</creatorcontrib><creatorcontrib>WANG DI</creatorcontrib><creatorcontrib>WANG BAOHUI</creatorcontrib><creatorcontrib>WEI FENG</creatorcontrib><creatorcontrib>ZHAO JINXIONG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LI ZIYUAN</au><au>MA ZHICHENG</au><au>DANG QIAN</au><au>ZHANG LEI</au><au>ZHENG GUANGYUAN</au><au>ZHANG XUN</au><au>BAI WANRONG</au><au>WANG DI</au><au>WANG BAOHUI</au><au>WEI FENG</au><au>ZHAO JINXIONG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Cross-site scripting attack detection method and system based on deep learning</title><date>2023-07-25</date><risdate>2023</risdate><abstract>The invention discloses a cross-site scripting attack detection method and system based on deep learning. The method comprises the following steps: S1, acquiring XSS sample data in multiple channels; s2, performing sample construction based on the acquired XSS sample data and normal sample data, and preprocessing the constructed sample to generate a sample data set; s3, based on a deep learning method, establishing an improved model which connects a CNN model and a BiLSTM model in series and combines an attention mechanism, and extracting sample data set features; s4, utilizing a feature vector obtained by carrying out transfer learning through BERT to accelerate convergence of the model, and improving the detection efficiency; and S5, deploying an improved model which connects the CNN model and the BiLSTM model in series and combines an attention mechanism, collecting data, and carrying out cross-site scripting attack detection. According to the scheme, the accuracy rate, the recall rate and the precision ra</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN116488898A |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC COMMUNICATION TECHNIQUE ELECTRICITY PHYSICS TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION |
| title | Cross-site scripting attack detection method and system based on deep learning |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-18T22%3A40%3A11IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=LI%20ZIYUAN&rft.date=2023-07-25&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN116488898A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |