{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T03:07:44Z","timestamp":1778123264274,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":11,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T00:00:00Z","timestamp":1573084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Army Research Laboratory","award":["W911NF-13-2-0045"],"award-info":[{"award-number":["W911NF-13-2-0045"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,11,7]]},"DOI":"10.1145\/3318216.3363338","type":"proceedings-article","created":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T14:11:35Z","timestamp":1572876695000},"page":"383-388","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["A moving target defense against adversarial machine learning"],"prefix":"10.1145","author":[{"given":"Abhishek","family":"Roy","sequence":"first","affiliation":[{"name":"University of California, Davis"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anshuman","family":"Chhabra","sequence":"additional","affiliation":[{"name":"University of California, Davis"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charles A.","family":"Kamhoua","sequence":"additional","affiliation":[{"name":"Network Security Branch, U.S. Army Research Laboratory (ARL)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prasant","family":"Mohapatra","sequence":"additional","affiliation":[{"name":"University of California, Davis"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,11,7]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. arXiv preprint arXiv:1605.07277","author":"Papernot Nicolas","year":"2016","unstructured":"Nicolas Papernot , Patrick McDaniel , and Ian Goodfellow . Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. arXiv preprint arXiv:1605.07277 , 2016 . Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow. Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. arXiv preprint arXiv:1605.07277, 2016."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"e_1_3_2_1_3_1","volume-title":"Black-box adversarial attacks with limited queries and information. arXiv preprint arXiv:1804.08598","author":"Ilyas Andrew","year":"2018","unstructured":"Andrew Ilyas , Logan Engstrom , Anish Athalye , and Jessy Lin . Black-box adversarial attacks with limited queries and information. arXiv preprint arXiv:1804.08598 , 2018 . Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin. Black-box adversarial attacks with limited queries and information. arXiv preprint arXiv:1804.08598, 2018."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3052973.3053009"},{"key":"e_1_3_2_1_5_1","volume-title":"Breaking transferability of adversarial samples with randomness. arXiv preprint arXiv:1805.04613","author":"Zhou Yan","year":"2018","unstructured":"Yan Zhou , Murat Kantarcioglu , and Bowei Xi . Breaking transferability of adversarial samples with randomness. arXiv preprint arXiv:1805.04613 , 2018 . Yan Zhou, Murat Kantarcioglu, and Bowei Xi. Breaking transferability of adversarial samples with randomness. arXiv preprint arXiv:1805.04613, 2018."},{"key":"e_1_3_2_1_6_1","volume-title":"Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence","author":"Sengupta Sailik","year":"2018","unstructured":"Sailik Sengupta , Tathagata Chakraborti , and Subbarao Kambhampati . Mtdeep : boosting the security of deep neural nets against adversarial attacks with moving target defense . In Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence , 2018 . Sailik Sengupta, Tathagata Chakraborti, and Subbarao Kambhampati. Mtdeep: boosting the security of deep neural nets against adversarial attacks with moving target defense. In Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence, 2018."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1006\/cogp.1998.0710"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00122574"},{"key":"e_1_3_2_1_9_1","volume-title":"Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572","author":"Goodfellow Ian J","year":"2014","unstructured":"Ian J Goodfellow , Jonathon Shlens , and Christian Szegedy . Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 , 2014 . Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014."},{"key":"e_1_3_2_1_10_1","volume-title":"MNIST handwritten digit database","author":"LeCun Yann","year":"2010","unstructured":"Yann LeCun and Corinna Cortes . MNIST handwritten digit database . 2010 . Yann LeCun and Corinna Cortes. MNIST handwritten digit database. 2010."},{"key":"e_1_3_2_1_11_1","first-page":"1097","volume-title":"Advances in neural information processing systems","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey E Hinton . Imagenet classification with deep convolutional neural networks . In Advances in neural information processing systems , pages 1097 -- 1105 , 2012 . Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097--1105, 2012."}],"event":{"name":"SEC '19: The Fourth ACM\/IEEE Symposium on Edge Computing","location":"Arlington Virginia","acronym":"SEC '19","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing","IEEE-CS\\DATC IEEE Computer Society"]},"container-title":["Proceedings of the 4th ACM\/IEEE Symposium on Edge Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3318216.3363338","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3318216.3363338","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3318216.3363338","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:54:40Z","timestamp":1750204480000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3318216.3363338"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,7]]},"references-count":11,"alternative-id":["10.1145\/3318216.3363338","10.1145\/3318216"],"URL":"https:\/\/doi.org\/10.1145\/3318216.3363338","relation":{},"subject":[],"published":{"date-parts":[[2019,11,7]]},"assertion":[{"value":"2019-11-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}