{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T12:08:51Z","timestamp":1730203731616,"version":"3.28.0"},"reference-count":24,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,1,6]],"date-time":"2024-01-06T00:00:00Z","timestamp":1704499200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,6]],"date-time":"2024-01-06T00:00:00Z","timestamp":1704499200000},"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":[[2024,1,6]]},"DOI":"10.1109\/ccnc51664.2024.10454779","type":"proceedings-article","created":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T18:53:49Z","timestamp":1710788029000},"page":"339-344","source":"Crossref","is-referenced-by-count":1,"title":["Malicious Log Detection Using Machine Learning to Maximize the Partial AUC"],"prefix":"10.1109","author":[{"given":"Taishi","family":"Nishiyama","sequence":"first","affiliation":[{"name":"NTT Security Japan,Tokyo,Japan"}]},{"given":"Atsutoshi","family":"Kumagai","sequence":"additional","affiliation":[{"name":"NTT Laboratories,Tokyo,Japan"}]},{"given":"Akinori","family":"Fujino","sequence":"additional","affiliation":[{"name":"NTT Laboratories,Tokyo,Japan"}]},{"given":"Kazunori","family":"Kamiya","sequence":"additional","affiliation":[{"name":"NTT Security Japan,Tokyo,Japan"}]}],"member":"263","reference":[{"volume-title":"Avtest","year":"2023","key":"ref1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23204"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3274694.3274700"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3196494.3196511"},{"key":"ref5","article-title":"A structural svm based approach for optimizing partial auc","volume-title":"Proceedings of the 30th International Conference on Machine Learning, (ICML)","volume":"28","author":"Narasimhan"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-16-8531-6_3"},{"volume-title":"Scikit-learn","year":"2023","key":"ref7"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-45528-0"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015366"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74976-9_8"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487674"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1111\/1541-0420.00071"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/279232.279236"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66332-6_6"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66399-9_19"},{"issue":"6","key":"ref17","first-page":"446","article-title":"A study on nsl-kdd dataset for intrusion detection system based on classification algorithms","volume":"4","author":"Dhanabal","year":"2015","journal-title":"International Jour-nal of Advanced Research in Computer and Communication Engineering (IJARCCE)"},{"volume-title":"Virustotal","year":"2023","key":"ref18"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CSS.2011.6058563"},{"volume-title":"ESET Homepage","year":"2023","key":"ref20"},{"volume-title":"AWS","year":"2023","key":"ref21"},{"key":"ref22","first-page":"807","article-title":"Optimized invariant representation of network traffic for detecting unseen mal ware variants","volume-title":"Proceedings of the 25th USENIX Security Symposium","author":"Bartos"},{"key":"ref23","first-page":"589","article-title":"ExecScent: mining for new c&c domains in live networks with adaptive control protocol templates","volume-title":"Proceedings of the 22nd USENIX Security Symposium","author":"Nelms"},{"key":"ref24","article-title":"Malicious url detection using machine learning: a survey","author":"Sahoo","year":"2017","journal-title":"arXiv preprint"}],"event":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","start":{"date-parts":[[2024,1,6]]},"location":"Las Vegas, NV, USA","end":{"date-parts":[[2024,1,9]]}},"container-title":["2024 IEEE 21st Consumer Communications &amp; Networking Conference (CCNC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10454139\/10454627\/10454779.pdf?arnumber=10454779","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T13:11:59Z","timestamp":1711458719000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10454779\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,6]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/ccnc51664.2024.10454779","relation":{},"subject":[],"published":{"date-parts":[[2024,1,6]]}}}