{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T04:35:51Z","timestamp":1769315751936,"version":"3.49.0"},"reference-count":30,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"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":[[2019,12]]},"DOI":"10.1109\/bigdata47090.2019.9006061","type":"proceedings-article","created":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T06:05:34Z","timestamp":1582610734000},"page":"5921-5928","source":"Crossref","is-referenced-by-count":23,"title":["Gradient boosting decision trees for cyber security threats detection based on network events logs"],"prefix":"10.1109","author":[{"given":"Quang Hieu","family":"Vu","sequence":"first","affiliation":[]},{"given":"Dymitr","family":"Ruta","sequence":"additional","affiliation":[]},{"given":"Ling","family":"Cen","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref30","first-page":"2951","article-title":"Practical Bayesian Optimization of Machine Learning Algorithms","author":"snoek","year":"2012","journal-title":"Advances in Neural Information Processing System"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.23919\/INM.2017.7987433"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/EST.2017.8090392"},{"key":"ref12","first-page":"1009","article-title":"Cloudy with a chance of breach: Forecasting cyber security incidents","author":"liu","year":"2015","journal-title":"Proc Usenix Security Symp"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1613\/jair.614"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/MCAS.2006.1688199"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-009-9124-7"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/BF00058655"},{"key":"ref17","first-page":"512","article-title":"Boosting Algorithms as Gradient Descent","volume":"12","author":"mason","year":"0","journal-title":"Advances in neural information processing systems"},{"key":"ref18","first-page":"512","article-title":"Boosting algorithms as gradient descent","author":"mason","year":"1999","journal-title":"Proceedings of International Conference on Neural Information Processing Systems"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"friedman","year":"2001","journal-title":"Ann Statist"},{"key":"ref28","first-page":"6639","article-title":"CatBoost: unbiased boosting with categorical features","author":"prokhorenkova","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref4","year":"2017","journal-title":"Neural Information Processing Systems Conference (NIPS)"},{"key":"ref27","article-title":"An ensemble model with hierarchical de-composition and aggregation for highly scalable and robust classification","author":"vu","year":"2017","journal-title":"Proc Int Symp Advances in Artificial Intelligence and Applications"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9005668"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/4109836"},{"key":"ref29","article-title":"CatBoost: gradient boosting with categorical features support","author":"dorogush","year":"2017","journal-title":"NIPS ML Systems Workshop"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2871866"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/MILCOM.2015.7357562"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-5225-8304-2.ch002"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcss.2014.02.005"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.14257\/ijsia.2016.10.4.23"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/NCIA.2013.6725337"},{"key":"ref20","article-title":"LightGBM: A Highly Efficient Gradient Boosting Decision Tree","author":"ke","year":"2013","journal-title":"Proc"},{"key":"ref22","year":"2016","journal-title":"XGBoost Machine Learning Challenge Winning Solutions"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref24","year":"0","journal-title":"A unified approach to explain the output of any machine learning model"},{"key":"ref23","first-page":"4765","article-title":"A Unified Approach to Interpreting Model Predictions","author":"lundberg","year":"2017","journal-title":"Advances in Neural Information Processing Systems I (NIPS)"},{"key":"ref26","first-page":"12","article-title":"Predicting Win-rates of Hearthstone Decks: Models and Features that Won AAIA&#x2019;2018 Data Mining Challenge","author":"vu","year":"2017","journal-title":"Proc Int Symp Advances in Artificial Intelligence and 7th Int Conf Emerg Security Technol (EST)"},{"key":"ref25","article-title":"Regression networks for robust win-rates predictions of AI gaming bots","author":"cen","year":"2018","journal-title":"Proc Int Symp Advances in Artificial Intelligence and Applications"}],"event":{"name":"2019 IEEE International Conference on Big Data (Big Data)","location":"Los Angeles, CA, USA","start":{"date-parts":[[2019,12,9]]},"end":{"date-parts":[[2019,12,12]]}},"container-title":["2019 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8986695\/9005444\/09006061.pdf?arnumber=9006061","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,17]],"date-time":"2022-07-17T21:56:02Z","timestamp":1658094962000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9006061\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/bigdata47090.2019.9006061","relation":{},"subject":[],"published":{"date-parts":[[2019,12]]}}}