{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,24]],"date-time":"2025-08-24T01:46:04Z","timestamp":1755999964692},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030017453"},{"type":"electronic","value":"9783030017460"}],"license":[{"start":{"date-parts":[[2018,11,5]],"date-time":"2018-11-05T00:00:00Z","timestamp":1541376000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-01746-0_12","type":"book-chapter","created":{"date-parts":[[2018,11,4]],"date-time":"2018-11-04T18:22:18Z","timestamp":1541355738000},"page":"103-112","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Comparative Results with Unsupervised Techniques in Cyber Attack Novelty Detection"],"prefix":"10.1007","author":[{"given":"Jorge","family":"Meira","sequence":"first","affiliation":[]},{"given":"Rui","family":"Andrade","sequence":"additional","affiliation":[]},{"given":"Isabel","family":"Pra\u00e7a","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o","family":"Carneiro","sequence":"additional","affiliation":[]},{"given":"Goreti","family":"Marreiros","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,11,5]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","unstructured":"Zanero, S., Serazzi, G.: Unsupervised learning algorithms for intrusion detection. In: IEEE Network Operations and Management Symposium, NOMS 2008, pp. 1043\u20131048. IEEE (2008). https:\/\/doi.org\/10.1109\/noms.2008.4575276","DOI":"10.1109\/noms.2008.4575276"},{"key":"12_CR2","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1016\/j.comcom.2012.01.016","volume":"35","author":"P Casas","year":"2012","unstructured":"Casas, P., Mazel, J., Owezarski, P.: Unsupervised network intrusion detection systems: detecting the unknown without knowledge. Comput. Commun. 35, 772\u2013783 (2012). https:\/\/doi.org\/10.1016\/j.comcom.2012.01.016","journal-title":"Comput. Commun."},{"key":"12_CR3","unstructured":"SASSI Project. http:\/\/sassi.visiontechlab.com\/"},{"key":"12_CR4","doi-asserted-by":"publisher","unstructured":"Goldstein, M., Goldstein, M., Uchida, S.: A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PLoS One 1\u201331 (2016). https:\/\/doi.org\/10.7910\/dvn\/opqmvf","DOI":"10.7910\/dvn\/opqmvf"},{"key":"12_CR5","doi-asserted-by":"publisher","unstructured":"Aleroud, A., Karabatis, G.: Toward zero-day attack identification using linear data transformation techniques. In: Proceedings of the 7th International Conference on Software Security and Reliability, SERE 2013, pp. 159\u2013168 (2013). https:\/\/doi.org\/10.1109\/sere.2013.16","DOI":"10.1109\/sere.2013.16"},{"key":"12_CR6","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/s10618-011-0234-x","volume":"25","author":"K Noto","year":"2012","unstructured":"Noto, K., Brodley, C., Slonim, D.: FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection. Data Min. Knowl. Discov. 25, 109\u2013133 (2012). https:\/\/doi.org\/10.1007\/s10618-011-0234-x","journal-title":"Data Min. Knowl. Discov."},{"key":"12_CR7","unstructured":"UCI Machine Learning Repository: KDD Cup 1999 Data. 92697 (2015)"},{"key":"12_CR8","doi-asserted-by":"publisher","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: IEEE Symposium on Computational Intelligence in Security and Defense Applications, CISDA 2009, pp. 1\u20136 (2009). https:\/\/doi.org\/10.1109\/cisda.2009.5356528","DOI":"10.1109\/cisda.2009.5356528"},{"key":"12_CR9","doi-asserted-by":"crossref","unstructured":"Liu, H., Hussain, F., Tan, C.L.I.M., Dash, M.: Discretization: An Enabling Technique, pp. 393\u2013423 (2002)","DOI":"10.1023\/A:1016304305535"},{"key":"12_CR10","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.cose.2011.12.012","volume":"31","author":"A Shiravi","year":"2012","unstructured":"Shiravi, A., Shiravi, H., Tavallaee, M., Ghorbani, A.A.: Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput. Secur. 31, 357\u2013374 (2012). https:\/\/doi.org\/10.1016\/j.cose.2011.12.012","journal-title":"Comput. Secur."},{"key":"12_CR11","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1137\/1.9781611974973.11","volume-title":"Proceedings of the 2017 SIAM International Conference on Data Mining","author":"Jinghui Chen","year":"2017","unstructured":"Chen, J., Sathe, S., Aggarwal, C., Turaga, D.: Outlier detection with autoencoder ensembles. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 90\u201398 (2017)"},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Yoon, K.A., Kwon, O.S., Bae, D.H.: An approach to outlier detection of software measurement data using the K-means clustering method. In: First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007), pp. 443\u2013445 (2007)","DOI":"10.1109\/ESEM.2007.49"},{"key":"12_CR13","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/3-540-45681-3_2","volume-title":"Principles of Data Mining and Knowledge Discovery","author":"Fabrizio Angiulli","year":"2002","unstructured":"Angiulli, F., Pizzuti, C.: Fast outlier detection in high dimensional spaces. Princ. Data Min. Knowl. Discov. 15\u201327 (2002). https:\/\/doi.org\/10.1007\/3-540-45681-3_2"},{"issue":"1","key":"12_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2133360.2133363","volume":"6","author":"Fei Tony Liu","year":"2012","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.-H.: Isolation-based anomaly detection. ACM Trans. Knowl. Discov. Data 6, 3:1\u20133:39 (2012). https:\/\/doi.org\/10.1145\/2133360.2133363","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"12_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5923\/j.ajis.20160601.01","volume":"4","author":"TM Mitchell","year":"2016","unstructured":"Mitchell, T.M., Tom, M., Hansson, K., Yella, S., Dougherty, M., Fleyeh, H., Pham, D.T., Afify, A.A., Wuest, T., Weimer, D., Irgens, C., Thoben, K.-D.: Machine learning in manufacturing: advantages, challenges, and applications. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 4, 1\u201313 (2016). https:\/\/doi.org\/10.5923\/j.ajis.20160601.01","journal-title":"Proc. Inst. Mech. Eng. Part B J. Eng. Manuf."}],"container-title":["Advances in Intelligent Systems and Computing","Ambient Intelligence \u2013 Software and Applications \u2013, 9th International Symposium on Ambient Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01746-0_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T16:02:35Z","timestamp":1572537755000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-01746-0_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,5]]},"ISBN":["9783030017453","9783030017460"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01746-0_12","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2018,11,5]]},"assertion":[{"value":"ISAmI2018","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Ambient Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toledo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 June 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 June 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isaml2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.isami-conference.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}