{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T17:57:16Z","timestamp":1773511036020,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":7,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789811982842","type":"print"},{"value":"9789811982859","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,12,10]],"date-time":"2022-12-10T00:00:00Z","timestamp":1670630400000},"content-version":"vor","delay-in-days":343,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>With the development of the Internet, cyber security events occur frequently, especially webpage tampering events account for a high proportion. In response to this phenomenon, this paper constructs a webpage tampering detection framework BCR. Based on the webpage to be detected, the webpage text data is segmented and extracted according to the webpage structure, the text features are extracted by using BiGRU model combined with context dependence, and then combined with the CRF to learn sequence state labeling named entities, the word vector is constructed by the extracted named entity and brought into the RCNN model for tampering detection. The experiment results show that the framework has achieved 95.37% precision, 95.35% recall and 95.34% F1-Score in webpage tampering detection, which is better than Textrank RCNN framework in webpage tampering detection. In practical application, it also achieved 95.13% precision and 93.25% recall.<\/jats:p>","DOI":"10.1007\/978-981-19-8285-9_8","type":"book-chapter","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T20:02:48Z","timestamp":1670616168000},"page":"113-126","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Webpage Tampering Detection Method Based on BiGRU-CRF-RCNN"],"prefix":"10.1007","author":[{"given":"Xiangyu","family":"Fan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jilong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jincheng","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,10]]},"reference":[{"issue":"6","key":"8_CR1","first-page":"5","volume":"48","author":"Y Yan","year":"2020","unstructured":"Yan, Y., Shen, Y.: Web page tamper detection based on image processing. Comput. Digit. Eng. 48(6), 5 (2020)","journal-title":"Comput. Digit. Eng."},{"key":"8_CR2","unstructured":"Hongwei, R., Liu, T., Hua, L., et al.: Webpage tamper detection based on principal component analysis. China Sci. Pap. (2012)"},{"key":"8_CR3","doi-asserted-by":"crossref","unstructured":"Chiu, J., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. Comput. Sci. 4, 357-370 (2015)","DOI":"10.1162\/tacl_a_00104"},{"key":"8_CR4","unstructured":"Fan, X., Zhou, A., Zheng, R., et al.: Darknet market named entity recognition based on deep learning. J. Inf. Secur. Res. (2021)"},{"issue":"1","key":"8_CR5","first-page":"12","volume":"55","author":"Y Qin","year":"2019","unstructured":"Qin, Y., Shen, G., Zhao, W., et al.: Research on the method of network security entity recognition based on deep neural network. J. Nanjing Univ. Natl. Sci. 55(1), 12 (2019)","journal-title":"J. Nanjing Univ. Natl. Sci."},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Yi, F., Jiang, B., Wang, L., et al.: Cybersecurity named entity recognition using multi-modal ensemble learning. IEEE Access, (99), 1\u20131 (2020)","DOI":"10.1109\/ACCESS.2020.2984582"},{"key":"8_CR7","unstructured":"Li, K.-Y., Liu, X.-D.: Web tampering detection system based on feature recognition. Electron. Des. Eng. 28(440)(18), 22\u201325+30 (2020)"}],"container-title":["Communications in Computer and Information Science","Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-8285-9_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,24]],"date-time":"2022-12-24T00:03:04Z","timestamp":1671840184000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-8285-9_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811982842","9789811982859"],"references-count":7,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-8285-9_8","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"10 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CNCERT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China Cyber Security Annual Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cncert2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/conf.cert.org.cn","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}