{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T01:43:40Z","timestamp":1774057420963,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["CNS 2327427"],"award-info":[{"award-number":["CNS 2327427"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009193","name":"Marsden Fund","doi-asserted-by":"publisher","award":["VUW2213"],"award-info":[{"award-number":["VUW2213"]}],"id":[{"id":"10.13039\/501100009193","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,9,30]]},"DOI":"10.1145\/3678890.3678893","type":"proceedings-article","created":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T22:23:36Z","timestamp":1727648616000},"page":"97-113","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Cross-Regional Malware Detection via Model Distilling and Federated Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6870-1178","authenticated-orcid":false,"given":"Marcus","family":"Botacin","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, Texas A&M University, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5276-637X","authenticated-orcid":false,"given":"Heitor","family":"Gomes","sequence":"additional","affiliation":[{"name":"Victoria University of Wellington, New Zealand"}]}],"member":"320","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Dos and Don\u2019ts of Machine Learning in Computer Security. In 31st USENIX Security Symposium (USENIX Security 22)","author":"Arp Daniel","year":"2022","unstructured":"Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, and Konrad Rieck. 2022. Dos and Don\u2019ts of Machine Learning in Computer Security. In 31st USENIX Security Symposium (USENIX Security 22). USENIX Association, Boston, MA, 3971\u20133988. https:\/\/www.usenix.org\/conference\/usenixsecurity22\/presentation\/arp"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/AICCSA.2014.7073259"},{"key":"e_1_3_2_1_3_1","volume-title":"Early Drift Detection Method. Fourth International Workshop on Knowledge Discovery from Data Streams 1 (01","author":"Baena-Garc\u00eda Manuel","year":"2006","unstructured":"Manuel Baena-Garc\u00eda, Jos\u00e9 Campo-\u00c1vila, Ra\u00fal Fidalgo-Merino, Albert Bifet, Ricard Gavald, and Rafael Morales-Bueno. 2006. Early Drift Detection Method. Fourth International Workshop on Knowledge Discovery from Data Streams 1 (01 2006), 1."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833659"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/1756006.1859903"},{"key":"e_1_3_2_1_6_1","volume-title":"USENIX Enigma","author":"Botacin Marcus","unstructured":"Marcus Botacin. 2021. Does Your Threat Model Consider Country and Culture? A Case Study of Brazilian Internet Banking Security to Show That It Should!. In USENIX Enigma. USENIX Association, USA, 1."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3429741"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2020.101859"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102287"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102500"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3339252.3340103"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118590"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3433667.3433669"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","unstructured":"Fabr\u00edcio Ceschin Heitor\u00a0Murilo Gomes Marcus Botacin Albert Bifet Bernhard Pfahringer Luiz\u00a0S. Oliveira and Andr\u00e9 Gr\u00e9gio. 2020. Machine Learning (In) Security: A Stream of Problems. https:\/\/doi.org\/10.48550\/ARXIV.2010.16045","DOI":"10.48550\/ARXIV.2010.16045"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSEC.2018.2875369"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2989055"},{"key":"e_1_3_2_1_17_1","volume-title":"29th USENIX security symposium (USENIX Security 20). USENIX, US, 1605\u20131622.","author":"Fang Minghong","unstructured":"Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Gong. 2020. Local model poisoning attacks to { Byzantine-Robust} federated learning. In 29th USENIX security symposium (USENIX Security 20). USENIX, US, 1605\u20131622."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/SPW53761.2021.00017"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-017-5642-8"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01453-z"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66399-9_4"},{"key":"e_1_3_2_1_22_1","volume-title":"Transcend: Detecting Concept Drift in Malware Classification Models. In 26th USENIX Security Symposium (USENIX Security 17)","author":"Jordaney Roberto","year":"2017","unstructured":"Roberto Jordaney, Kumar Sharad, Santanu\u00a0K. Dash, Zhi Wang, Davide Papini, Ilia Nouretdinov, and Lorenzo Cavallaro. 2017. Transcend: Detecting Concept Drift in Malware Classification Models. In 26th USENIX Security Symposium (USENIX Security 17). USENIX Association, Vancouver, BC, 625\u2013642. https:\/\/www.usenix.org\/conference\/usenixsecurity17\/technical-sessions\/presentation\/jordaney"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02451-x"},{"key":"e_1_3_2_1_24_1","volume-title":"Fedmd: Heterogenous federated learning via model distillation. arXiv preprint arXiv:1910.03581 1","author":"Li Daliang","year":"2019","unstructured":"Daliang Li and Junpu Wang. 2019. Fedmd: Heterogenous federated learning via model distillation. arXiv preprint arXiv:1910.03581 1 (2019), 1."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.23919\/ICACT48636.2020.9061261"},{"key":"e_1_3_2_1_26_1","first-page":"2351","article-title":"Ensemble distillation for robust model fusion in federated learning","volume":"33","author":"Lin Tao","year":"2020","unstructured":"Tao Lin, Lingjing Kong, Sebastian\u00a0U Stich, and Martin Jaggi. 2020. Ensemble distillation for robust model fusion in federated learning. Advances in Neural Information Processing Systems 33 (2020), 2351\u20132363.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3495922"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-23095-0_7"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-40667-1_7"},{"key":"e_1_3_2_1_30_1","unstructured":"MOA. 2017. AdaptiveRandomForest. https:\/\/moa.cms.waikato.ac.nz\/adaptive-random-forest\/."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102996"},{"key":"e_1_3_2_1_32_1","volume-title":"28th USENIX Security Symposium (USENIX Security 19)","author":"Pendlebury Feargus","year":"2019","unstructured":"Feargus Pendlebury, Fabio Pierazzi, Roberto Jordaney, Johannes Kinder, and Lorenzo Cavallaro. 2019. TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time. In 28th USENIX Security Symposium (USENIX Security 19). USENIX Association, Santa Clara, CA, 729\u2013746. https:\/\/www.usenix.org\/conference\/usenixsecurity19\/presentation\/pendlebury"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.fsidi.2021.301183"},{"key":"e_1_3_2_1_34_1","unstructured":"scikit learn. 2020. Machine Learning in Python. https:\/\/scikit-learn.org\/stable\/."},{"key":"e_1_3_2_1_35_1","unstructured":"scikit learn. 2020. SelectKBest. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.feature_selection.SelectKBest.html."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/LES.2022.3207968"},{"key":"e_1_3_2_1_37_1","volume-title":"ESORICS 2020, Guildford, UK, September 14\u201318, 2020, Proceedings, Part I 25","author":"Tolpegin Vale","year":"2020","unstructured":"Vale Tolpegin, Stacey Truex, Mehmet\u00a0Emre Gursoy, and Ling Liu. 2020. Data poisoning attacks against federated learning systems. In Computer Security\u2013ESORICS 2020: 25th European Symposium on Research in Computer Security, ESORICS 2020, Guildford, UK, September 14\u201318, 2020, Proceedings, Part I 25. Springer, UK, 480\u2013501."},{"key":"e_1_3_2_1_38_1","unstructured":"VirusTotal. 2016. The pattern matching Swiss knife. https:\/\/github.com\/VirusTotal\/yara."},{"key":"e_1_3_2_1_39_1","unstructured":"Virustotal. 2023. Virustotal. https:\/\/www.virustotal.com\/gui\/home\/upload."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2019.00048"}],"event":{"name":"RAID '24: The 27th International Symposium on Research in Attacks, Intrusions and Defenses","location":"Padua Italy","acronym":"RAID '24"},"container-title":["The 27th International Symposium on Research in Attacks, Intrusions and Defenses"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3678890.3678893","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3678890.3678893","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3678890.3678893","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:00Z","timestamp":1750295880000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3678890.3678893"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"references-count":40,"alternative-id":["10.1145\/3678890.3678893","10.1145\/3678890"],"URL":"https:\/\/doi.org\/10.1145\/3678890.3678893","relation":{},"subject":[],"published":{"date-parts":[[2024,9,30]]},"assertion":[{"value":"2024-09-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}