{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:47:17Z","timestamp":1750308437568,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":9,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,12,16]]},"DOI":"10.1145\/3584376.3584606","type":"proceedings-article","created":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T22:54:51Z","timestamp":1681944891000},"page":"1304-1310","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Target adversarial sample generation for malicious traffic classification model"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8441-9534","authenticated-orcid":false,"given":"Zhenxiang","family":"He","sequence":"first","affiliation":[{"name":"Gansu University of Political Science and Law, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3001-4919","authenticated-orcid":false,"given":"Sen","family":"Fu","sequence":"additional","affiliation":[{"name":"Gansu University of Political Science and Law, China"}]}],"member":"320","published-online":{"date-parts":[[2023,4,19]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Cisco visual networking white paper","author":"Cisco","year":"2017","unstructured":"Cisco , \u201c The zettabyte era\u2013trends and analysis ,\u201d Cisco visual networking white paper , 2017 . Cisco, \u201cThe zettabyte era\u2013trends and analysis,\u201d Cisco visual networking white paper, 2017."},{"key":"e_1_3_2_1_2_1","volume-title":"Yann LeCun. 3rd International Conference on Learning Representations","author":"Goodfellow IJ","year":"2015","unstructured":"Goodfellow IJ , Shlens J, Szegedy C. Explaining and Harnessing Adversarial Examples[C]\/\/Yoshua Bengio , Yann LeCun. 3rd International Conference on Learning Representations , San Diego, CA, USA , May 7-9, 2015 . Goodfellow IJ, Shlens J, Szegedy C. Explaining and Harnessing Adversarial Examples[C]\/\/Yoshua Bengio, Yann LeCun. 3rd International Conference on Learning Representations, San Diego, CA, USA, May 7-9, 2015."},{"key":"e_1_3_2_1_3_1","volume-title":"\u201cAdversarial Machine Learning at Scale","author":"Kurakin Alexey","year":"2016","unstructured":"Alexey Kurakin , Ian Goodfellow and Samy Bengio . \u201cAdversarial Machine Learning at Scale \u201d arXiv: Computer Vision and Pattern Recognition , 2016 : n. pag. Alexey Kurakin, Ian Goodfellow and Samy Bengio. \u201cAdversarial Machine Learning at Scale\u201d arXiv: Computer Vision and Pattern Recognition, 2016: n. pag."},{"key":"e_1_3_2_1_4_1","volume-title":"\u201cThe Limitations of Deep Learning in Adversarial Settings","author":"Papernot Nicolas","year":"2015","unstructured":"Nicolas Papernot , Patrick McDaniel , Somesh Jha , Matt Fredrikson , Z. Berkay Celik and Ananthram Swami . \u201cThe Limitations of Deep Learning in Adversarial Settings \u201d arXiv: Cryptography and Security , 2015 : n. pag. Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik and Ananthram Swami. \u201cThe Limitations of Deep Learning in Adversarial Settings\u201d arXiv: Cryptography and Security, 2015: n. pag."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"e_1_3_2_1_6_1","volume-title":"\u201cDeepFool: a simple and accurate method to fool deep neural networks","author":"Moosavi-Dezfooli Seyed-Mohsen","year":"2015","unstructured":"Seyed-Mohsen Moosavi-Dezfooli , Alhussein Fawzi and Pascal Frossard . \u201cDeepFool: a simple and accurate method to fool deep neural networks \u201d arXiv: Learning , 2015 : n. pag. Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi and Pascal Frossard. \u201cDeepFool: a simple and accurate method to fool deep neural networks\u201d arXiv: Learning, 2015: n. pag."},{"key":"e_1_3_2_1_7_1","volume-title":"\u201cTransferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples","author":"Papernot Nicolas","year":"2016","unstructured":"Nicolas Papernot , Patrick McDaniel and Ian Goodfellow . \u201cTransferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples \u201d arXiv: Cryptography and Security , 2016 : n. pag. Nicolas Papernot, Patrick McDaniel and Ian Goodfellow. \u201cTransferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples\u201d arXiv: Cryptography and Security, 2016: n. pag."},{"key":"e_1_3_2_1_8_1","volume-title":"\u201cAdversarial Perturbations Against Deep Neural Networks for Malware Classification","author":"Grosse Kathrin","year":"2016","unstructured":"Kathrin Grosse , Nicolas Papernot , Praveen Manoharan , Michael Backes and Patrick McDaniel . \u201cAdversarial Perturbations Against Deep Neural Networks for Malware Classification \u201d arXiv: Cryptography and Security , 2016 : n. pag. Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes and Patrick McDaniel. \u201cAdversarial Perturbations Against Deep Neural Networks for Malware Classification\u201d arXiv: Cryptography and Security, 2016: n. pag."},{"key":"e_1_3_2_1_9_1","volume-title":"Adversarial sample generation for Avoiding botnet Traffic Detection [J\/OL]. Computer Engineering and Applications :1-9","author":"Peiyang Li","year":"2022","unstructured":"Li Peiyang , Li Xuan , Chen Junjie , Chen Yongle . Adversarial sample generation for Avoiding botnet Traffic Detection [J\/OL]. Computer Engineering and Applications :1-9 , 2022 , 01, 20. Li Peiyang, Li Xuan, Chen Junjie, Chen Yongle. Adversarial sample generation for Avoiding botnet Traffic Detection [J\/OL]. Computer Engineering and Applications :1-9, 2022, 01, 20."}],"event":{"name":"RICAI 2022: 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence","acronym":"RICAI 2022","location":"Dongguan China"},"container-title":["Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3584376.3584606","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3584376.3584606","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T17:49:58Z","timestamp":1750268998000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3584376.3584606"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,16]]},"references-count":9,"alternative-id":["10.1145\/3584376.3584606","10.1145\/3584376"],"URL":"https:\/\/doi.org\/10.1145\/3584376.3584606","relation":{},"subject":[],"published":{"date-parts":[[2022,12,16]]},"assertion":[{"value":"2023-04-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}