{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T17:30:40Z","timestamp":1762018240633,"version":"3.44.0"},"reference-count":16,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,8,1]],"date-time":"2019-08-01T00:00:00Z","timestamp":1564617600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,8,1]],"date-time":"2019-08-01T00:00:00Z","timestamp":1564617600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"DOI":"10.1109\/pst47121.2019.8949034","type":"proceedings-article","created":{"date-parts":[[2020,1,7]],"date-time":"2020-01-07T23:06:06Z","timestamp":1578438366000},"page":"1-5","source":"Crossref","is-referenced-by-count":11,"title":["On Embedding Backdoor in Malware Detectors Using Machine Learning"],"prefix":"10.1109","author":[{"given":"Shoichiro","family":"Sasaki","sequence":"first","affiliation":[{"name":"Graduate School of Information Sciences, Tohoku University,Sendai,Japan"}]},{"given":"Seira","family":"Hidano","sequence":"additional","affiliation":[{"name":"KDDI Research, Inc.,Fujimino,Japan"}]},{"given":"Toshihiro","family":"Uchibayashi","sequence":"additional","affiliation":[{"name":"Graduate School of Information Sciences, Tohoku University,Sendai,Japan"}]},{"given":"Takuo","family":"Suganuma","sequence":"additional","affiliation":[{"name":"Cyberscience Center, Tohoku University,Sendai,Japan"}]},{"given":"Masahiro","family":"Hiji","sequence":"additional","affiliation":[{"name":"Graduate School of Information Sciences, Tohoku University,Sendai,Japan"}]},{"given":"Shinsaku","family":"Kiyomoto","sequence":"additional","affiliation":[{"name":"KDDI Research, Inc.,Fujimino,Japan"}]}],"member":"263","reference":[{"key":"ref10","article-title":"Targeted backdoor attacks on deep learning systems using data poisoning","volume":"abs 1712 5526","author":"chen","year":"2017","journal-title":"CoRR"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/978-3-030-00470-5_13","article-title":"Fine-pruning: Defending against backdooring attacks on deep neural networks","volume":"11050","author":"liu","year":"2018","journal-title":"RAID ser Lecture Notes in Computer Science"},{"key":"ref12","first-page":"624","article-title":"Auto-mated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection","volume":"7","author":"sgandurra","year":"2016","journal-title":"Applied Clinical Informatics"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/MALWARE.2015.7413681"},{"journal-title":"EMBER An open dataset for training static PE malware machine learning models","year":"2018","author":"anderson","key":"ref14"},{"key":"ref15","article-title":"Mi-crosoft malware classification challenge","volume":"abs 1802 10135","author":"ronen","year":"2018","journal-title":"CoRR"},{"key":"ref16","first-page":"395","article-title":"A linear programming approach to novelty detection","author":"campbell","year":"2000","journal-title":"Proceedings of the 13th Annual Conference on Neural Information Processing Systems (NIPS)"},{"key":"ref4","first-page":"1467","article-title":"Poisoning attacks against support vector machines","author":"biggio","year":"2012","journal-title":"Proceedings of the 29th International Coference on International Conference on Machine Learning ser ICML'12"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2805680"},{"key":"ref6","first-page":"1689","article-title":"Is Feature Selection Secure against Training Data Poisoning?","volume":"37","author":"xiao","year":"2015","journal-title":"JMLR W&CP-Proc 32nd Int'l Conf Mach Learning (ICML)"},{"key":"ref5","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v29i1.9569","article-title":"Using machine teaching to identify optimal training-set attacks on machine learners","author":"mei","year":"2015","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)"},{"key":"ref8","first-page":"27","article-title":"Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization","author":"mu\u00f1oz-gonz\u00e1lez","year":"2017","journal-title":"Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security - AISEC'17"},{"key":"ref7","first-page":"1885","article-title":"Data poisoning attacks on factorization-based collaborative filtering","author":"li","year":"2016","journal-title":"Proceedings of the 30th Annual Conference on Neural Information Processing Systems (NIPS)"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3073559"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.5220\/0001863603170320"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2018.00057"}],"event":{"name":"2019 17th International Conference on Privacy, Security and Trust (PST)","start":{"date-parts":[[2019,8,26]]},"location":"Fredericton, NB, Canada","end":{"date-parts":[[2019,8,28]]}},"container-title":["2019 17th International Conference on Privacy, Security and Trust (PST)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8937293\/8949011\/08949034.pdf?arnumber=8949034","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T19:20:39Z","timestamp":1756754439000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8949034\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":16,"URL":"https:\/\/doi.org\/10.1109\/pst47121.2019.8949034","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}