{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T05:55:47Z","timestamp":1780466147620,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T00:00:00Z","timestamp":1745280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076125, 62172258, U21A20467"],"award-info":[{"award-number":["62076125, 62172258, U21A20467"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&D Program of China","award":["2022YFB3103500"],"award-info":[{"award-number":["2022YFB3103500"]}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20210324134810028"],"award-info":[{"award-number":["JCYJ20210324134810028"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,4,22]]},"DOI":"10.1145\/3696410.3714811","type":"proceedings-article","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T22:52:18Z","timestamp":1745362338000},"page":"1068-1077","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Beyond Single Tabs: A Transformative Few-Shot Approach to Multi-Tab Website Fingerprinting Attacks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9078-4518","authenticated-orcid":false,"given":"Wenwen","family":"Meng","sequence":"first","affiliation":[{"name":"Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7819-4544","authenticated-orcid":false,"given":"Chuan","family":"Ma","sequence":"additional","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3690-0321","authenticated-orcid":false,"given":"Ming","family":"Ding","sequence":"additional","affiliation":[{"name":"Data 61, CSIRO, Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9274-7325","authenticated-orcid":false,"given":"Chunpeng","family":"Ge","sequence":"additional","affiliation":[{"name":"Shandong University and Quan Cheng Laboratory, Shandong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9503-549X","authenticated-orcid":false,"given":"Yuwen","family":"Qian","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9439-4623","authenticated-orcid":false,"given":"Tao","family":"Xiang","sequence":"additional","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,4,22]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"15","article-title":"Fingerprinting attack on Tor anonymity using deep learning","volume":"42","author":"Abe Kota","year":"2016","unstructured":"Kota Abe and Shigeki Goto. 2016. Fingerprinting attack on Tor anonymity using deep learning. Proceedings of the Asia-Pacific Advanced Network, Vol. 42, 0 (2016), 15--20.","journal-title":"Proceedings of the Asia-Pacific Advanced Network"},{"key":"e_1_3_2_1_2_1","volume-title":"Var-CNN: A data-efficient website fingerprinting attack based on deep learning. arXiv preprint arXiv:1802.10215","author":"Bhat Sanjit","year":"2018","unstructured":"Sanjit Bhat, David Lu, Albert Kwon, and Srinivas Devadas. 2018. Var-CNN: A data-efficient website fingerprinting attack based on deep learning. arXiv preprint arXiv:1802.10215 (2018)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660267.2660362"},{"key":"e_1_3_2_1_4_1","volume-title":"Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407","author":"Chalapathy Raghavendra","year":"2019","unstructured":"Raghavendra Chalapathy and Sanjay Chawla. 2019. Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407 (2019)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3321705.3329802"},{"key":"e_1_3_2_1_6_1","volume-title":"Robust and reliable early-stage Website fingerprinting attacks via spatial-temporal distribution analysis. arXiv preprint arXiv:2407.00918","author":"Deng Xinhao","year":"2024","unstructured":"Xinhao Deng, Qi Li, and Ke Xu. 2024. Robust and reliable early-stage Website fingerprinting attacks via spatial-temporal distribution analysis. arXiv preprint arXiv:2407.00918 (2024)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP46215.2023.10179464"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CSCWD.2015.7230964"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2023.103627"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485832.3485891"},{"key":"e_1_3_2_1_11_1","volume-title":"25th USENIX Security Symposium (USENIX Security 16)","author":"Hayes Jamie","year":"2016","unstructured":"Jamie Hayes and George Danezis. 2016. k-fingerprinting: A robust scalable website fingerprinting technique. In 25th USENIX Security Symposium (USENIX Security 16). 1187--1203."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583200"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3576915.3623107"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660267.2660368"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080834"},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Machine Learning. PMLR, 23103--23123","author":"Luo Xu","year":"2023","unstructured":"Xu Luo, Hao Wu, Ji Zhang, Lianli Gao, Jing Xu, and Jingkuan Song. 2023. A closer look at few-shot classification again. In International Conference on Machine Learning. PMLR, 23103--23123."},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the Internet Measurement Conference","author":"Mani Akshaya","year":"2018","unstructured":"Akshaya Mani, T Wilson-Brown, Rob Jansen, Aaron Johnson, and Micah Sherr. 2018. Understanding tor usage with privacy-preserving measurement. In Proceedings of the Internet Measurement Conference 2018. 175--187."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Andriy Panchenko Fabian Lanze Jan Pennekamp Thomas Engel Andreas Zinnen Martin Henze and Klaus Wehrle. 2016. Website Fingerprinting at Internet Scale.. In NDSS.","DOI":"10.14722\/ndss.2016.23477"},{"key":"e_1_3_2_1_19_1","volume-title":"Kantha Girish Gangadhara, and Matthew Wright","author":"Rahman Mohammad Saidur","year":"2019","unstructured":"Mohammad Saidur Rahman, Payap Sirinam, Nate Mathews, Kantha Girish Gangadhara, and Matthew Wright. 2019. Tik-tok: The utility of packet timing in website fingerprinting attacks. arXiv preprint arXiv:1902.06421 (2019)."},{"key":"e_1_3_2_1_20_1","volume-title":"Tom Van Goethem, and Wouter Joosen","author":"Rimmer Vera","year":"2017","unstructured":"Vera Rimmer, Davy Preuveneers, Marc Juarez, Tom Van Goethem, and Wouter Joosen. 2017. Automated website fingerprinting through deep learning. arXiv preprint arXiv:1708.06376 (2017)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243768"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3354217"},{"key":"e_1_3_2_1_23_1","first-page":"1773","article-title":"Webpage fingerprinting identification on tor: a survey","volume":"58","author":"Sun Xueliang","year":"2021","unstructured":"Xueliang Sun, Anxin Huang, Xiapu Luo, and Yi Xie. 2021. Webpage fingerprinting identification on tor: a survey. Journal of Computer Research and Development, Vol. 58, 8 (2021), 1773--1788.","journal-title":"Journal of Computer Research and Development"},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings, Part III 27","author":"Tan Chuanqi","year":"2018","unstructured":"Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu. 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning--ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4--7, 2018, Proceedings, Part III 27. Springer, 270--279."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/WI-IAT.2013.8"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3422337.3447835"},{"key":"e_1_3_2_1_27_1","volume-title":"23rd USENIX Security Symposium (USENIX Security 14)","author":"Wang Tao","year":"2014","unstructured":"Tao Wang, Xiang Cai, Rishab Nithyanand, Rob Johnson, and Ian Goldberg. 2014. Effective attacks and provable defenses for website fingerprinting. In 23rd USENIX Security Symposium (USENIX Security 14). 143--157."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2517840.2517851"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2016-0027"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645575"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3274694.3274697"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2021.3104869"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645591"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2981333"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.3004555"}],"event":{"name":"WWW '25: The ACM Web Conference 2025","location":"Sydney NSW Australia","acronym":"WWW '25","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM on Web Conference 2025"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696410.3714811","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3696410.3714811","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:42Z","timestamp":1750295922000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696410.3714811"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,22]]},"references-count":35,"alternative-id":["10.1145\/3696410.3714811","10.1145\/3696410"],"URL":"https:\/\/doi.org\/10.1145\/3696410.3714811","relation":{},"subject":[],"published":{"date-parts":[[2025,4,22]]},"assertion":[{"value":"2025-04-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}