{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:37:19Z","timestamp":1742913439747,"version":"3.40.3"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031474507"},{"type":"electronic","value":"9783031474514"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-47451-4_41","type":"book-chapter","created":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T20:02:04Z","timestamp":1698782524000},"page":"574-589","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Controversial Study on\u00a0Random Forest Accuracy for\u00a0Attack Detection"],"prefix":"10.1007","author":[{"given":"Quentin","family":"Vacher","sequence":"first","affiliation":[]},{"given":"Philippe","family":"Owezarski","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,1]]},"reference":[{"key":"41_CR1","doi-asserted-by":"crossref","unstructured":"Elmrabit, N., Zhou, F., Li, F., Zhou, H.: Evaluation of machine learning algorithms for anomaly detection. In: IEEE International Conference on Cyber Security and Protection of Digital Services (Cyber Security) (2020)","DOI":"10.1109\/CyberSecurity49315.2020.9138871"},{"issue":"8","key":"41_CR2","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","volume":"31","author":"AK Jain","year":"2010","unstructured":"Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651\u2013666 (2010)","journal-title":"Pattern Recogn. Lett."},{"issue":"1","key":"41_CR3","doi-asserted-by":"publisher","DOI":"10.1002\/ett.4150","volume":"32","author":"Z Ahmad","year":"2021","unstructured":"Ahmad, Z., Khan, A.S., Shiang, C.W., Abdullah, J., Ahmad, F.: Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Trans. Emerg. Telecommun. Technol. 32(1), e4150 (2021)","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"41_CR4","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.comnet.2019.01.023","volume":"151","author":"AP Kelton","year":"2019","unstructured":"Kelton, A.P., da Costa, J., Celso, O., Munoz, R., de Albuquerque, C.: Internet of Things: a survey on machine learning-based intrusion detection approaches. Comput. Netw. 151, 147\u2013157 (2019)","journal-title":"Comput. Netw."},{"key":"41_CR5","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1186\/s40537-021-00448-4","volume":"8","author":"F Laghrissi","year":"2021","unstructured":"Laghrissi, F., Douzi, S., Douzi, K., Hssina, B.: Intrusion detection systems using Long Short-Term Memory (LSTM). J. Big Data 8, 65 (2021)","journal-title":"J. Big Data"},{"key":"41_CR6","first-page":"17","volume":"10","author":"L Alsulaiman","year":"2021","unstructured":"Alsulaiman, L., Al-Ahmadi, S.: Performance evaluation of machine learning techniques for DoS detection. J. Wirel. Sens. Netw. 10, 17 (2021)","journal-title":"J. Wirel. Sens. Netw."},{"issue":"3","key":"41_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3178582","volume":"51","author":"PAA Resende","year":"2018","unstructured":"Resende, P.A.A., Drummond, A.C.: A survey of random forest based methods for intrusion detection systems. ACM Comput. Surv. 51(3), 1\u201336 (2018)","journal-title":"ACM Comput. Surv."},{"key":"41_CR8","doi-asserted-by":"crossref","unstructured":"Jiang, J., Wang, Q., Shi, Z., Lv, B., Qi, B.: RST-RF: a hybrid model based on rough set theory and random forest for network intrusion detection. In: Proceedings of the 2nd International Conference on Cryptography, Security and Privacy, pp. 77\u201381 (2018)","DOI":"10.1145\/3199478.3199489"},{"key":"41_CR9","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Lashkari, A.H., Ghorbani, A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: Proceedings of the 4th International Conference on Information Systems Security and Privacy - ICISSP, pp. 108\u2013116 (2018)","DOI":"10.5220\/0006639801080116"},{"key":"41_CR10","unstructured":"RandomForestClassifier Scikit Learn. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.RandomForestClas-sifier.html"},{"key":"41_CR11","unstructured":"RandomOverSampler Imbalanced Learn. https:\/\/imbalanced-learn.org\/stable\/references\/generated\/imblearn.over_sampling.Random-OverSampler.html"},{"key":"41_CR12","unstructured":"Understanding the decision tree structure Scikit Learn. https:\/\/imbalanced-learn.org\/stable\/references\/generated\/imblearn.over_sampling.RandomOverSampler.html"}],"container-title":["Lecture Notes in Networks and Systems","Proceedings of the Future Technologies Conference (FTC) 2023, Volume 2"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47451-4_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,13]],"date-time":"2024-04-13T18:08:11Z","timestamp":1713031691000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47451-4_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031474507","9783031474514"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47451-4_41","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FTC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Proceedings of the Future Technologies Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ftc2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/saiconference.com\/FTC","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}