{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T08:15:29Z","timestamp":1726042529981},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030298586"},{"type":"electronic","value":"9783030298593"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-29859-3_55","type":"book-chapter","created":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T16:03:53Z","timestamp":1566835433000},"page":"648-659","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Anomaly Detection Using Gaussian Mixture Probability Model to Implement Intrusion Detection System"],"prefix":"10.1007","author":[{"given":"Roberto","family":"Blanco","sequence":"first","affiliation":[]},{"given":"Pedro","family":"Malag\u00f3n","sequence":"additional","affiliation":[]},{"given":"Samira","family":"Briongos","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9 M.","family":"Moya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,26]]},"reference":[{"unstructured":"Axelsson, S.: Intrusion detection systems: a survey and taxonomy. Chalmers University of Technology, Tech. rep. (2000)","key":"55_CR1"},{"doi-asserted-by":"publisher","unstructured":"Bahrololum, M., Khaleghi, M.: Anomaly intrusion detection system using Gaussian mixture model. In: 2008 Third International Conference on Convergence and Hybrid Information Technology, November 2008, vol. 1, pp. 1162\u20131167. https:\/\/doi.org\/10.1109\/ICCIT.2008.17","key":"55_CR2","DOI":"10.1109\/ICCIT.2008.17"},{"doi-asserted-by":"publisher","unstructured":"Barkan, O., Averbuch, A.: Robust mixture models for anomaly detection. In: 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), September 2016, pp. 1\u20136. https:\/\/doi.org\/10.1109\/MLSP.2016.7738885","key":"55_CR3","DOI":"10.1109\/MLSP.2016.7738885"},{"doi-asserted-by":"publisher","unstructured":"Breunig, M.M., Kriegel, H., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Chen, W., Naughton, J.F., Bernstein, P.A. (eds.) Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 16\u201318 May 2000, Dallas, Texas, USA, pp. 93\u2013104. ACM (2000). https:\/\/doi.org\/10.1145\/342009.335388","key":"55_CR4","DOI":"10.1145\/342009.335388"},{"issue":"2","key":"55_CR5","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1109\/TSE.1987.232894","volume":"13","author":"DE Denning","year":"1987","unstructured":"Denning, D.E.: An intrusion-detection model. IEEE Trans. Softw. Eng. 13(2), 222\u2013232 (1987). https:\/\/doi.org\/10.1109\/TSE.1987.232894","journal-title":"IEEE Trans. Softw. Eng."},{"key":"55_CR6","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1016\/j.patcog.2017.09.037","volume":"74","author":"R Domingues","year":"2018","unstructured":"Domingues, R., Filippone, M., Michiardi, P., Zouaoui, J.: A comparative evaluation of outlier detection algorithms: experiments and analyses. Pattern Recogn. 74, 406\u2013421 (2018)","journal-title":"Pattern Recogn."},{"issue":"1","key":"55_CR7","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/TNSM.2016.2627340","volume":"14","author":"J Dromard","year":"2017","unstructured":"Dromard, J., Roudi\u00e8re, G., Owezarski, P.: Online and scalable unsupervised network anomaly detection method. IEEE Trans. Netw. Serv. Manage. 14(1), 34\u201347 (2017). https:\/\/doi.org\/10.1109\/TNSM.2016.2627340","journal-title":"IEEE Trans. Netw. Serv. Manage."},{"doi-asserted-by":"crossref","unstructured":"Heady, R., Luger, G., Maccabe, A., Servilla, M.: The architecture of a network level intrusion detection system. Tech. rep., Los Alamos National Lab., NM, United States, New Mexico University, Albuquerque (1990)","key":"55_CR8","DOI":"10.2172\/425295"},{"unstructured":"Hock, D., Kappes, M.: A self-learning network anomaly detection system using majority voting. In: Dowland, P., Furnell, S., Ghita, B.V. (eds.) Proceedings Tenth International Network Conference, INC 2014, Plymouth, UK, 8\u201310 July 2014, pp. 59\u201369. Plymouth University (2014). http:\/\/www.cscan.org\/openaccess\/?paperid=225","key":"55_CR9"},{"issue":"2","key":"55_CR10","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s10462-004-4304-y","volume":"22","author":"VJ Hodge","year":"2004","unstructured":"Hodge, V.J., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85\u2013126 (2004). https:\/\/doi.org\/10.1007\/s10462-004-4304-y","journal-title":"Artif. Intell. Rev."},{"unstructured":"Kdd cup 1999, October 2007. http:\/\/kdd.ics.uci.edu\/databases\/kddcup99\/kddcup99.html","key":"55_CR11"},{"issue":"1","key":"55_CR12","first-page":"2529","volume":"13","author":"J Kim","year":"2012","unstructured":"Kim, J., Scott, C.D.: Robust kernel density estimation. J. Mach. Learn. Res. 13(1), 2529\u20132565 (2012). http:\/\/dl.acm.org\/citation.cfm?id=2503308.2503323","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"55_CR13","first-page":"51","volume":"10","author":"P Kukielka","year":"2010","unstructured":"Kukielka, P., Kotulski, Z.: Analysis of neural networks usage for detection of a new attack in IDS. Ann. UMCS Inf. 10(1), 51\u201359 (2010)","journal-title":"Ann. UMCS Inf."},{"doi-asserted-by":"publisher","unstructured":"Liu, D., Lung, C., Lambadaris, I., Seddigh, N.: Network traffic anomaly detection using clustering techniques and performance comparison. In: 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), May 2013, pp. 1\u20134. https:\/\/doi.org\/10.1109\/CCECE.2013.6567739","key":"55_CR14","DOI":"10.1109\/CCECE.2013.6567739"},{"doi-asserted-by":"crossref","unstructured":"Moustafa, N., Slay, J.: UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: Military Communications and Information Systems Conference (MilCIS), pp. 1\u20136. IEEE Stream (2015)","key":"55_CR15","DOI":"10.1109\/MilCIS.2015.7348942"},{"issue":"1\u201313","key":"55_CR16","first-page":"1","volume":"25","author":"N Moustafa","year":"2016","unstructured":"Moustafa, N., Slay, J.: The evaluation of network anomaly detection systems: statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set. Inf. Secur. J. A Global Perspect. 25(1\u201313), 1\u201314 (2016)","journal-title":"Inf. Secur. J. A Global Perspect."},{"unstructured":"NSL-KDD data set for network-based intrusion detection systems, March 2009. http:\/\/nsl.cs.unb.ca\/NSL-KDD\/","key":"55_CR17"},{"key":"55_CR18","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"12","key":"55_CR19","first-page":"1848","volume":"2","author":"S Revathi","year":"2013","unstructured":"Revathi, S., Malathi, A.: A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection. Int. J. Eng. Res. Tech. 2(12), 1848\u20131853 (2013)","journal-title":"Int. J. Eng. Res. Tech."},{"key":"55_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-73003-5","volume-title":"Encyclopedia of Biometrics","author":"DD Reynolds","year":"2009","unstructured":"Reynolds, D.D.: Gaussian Mixture Models. In: Li, S.Z., Jain, A. (eds.) Encyclopedia of Biometrics. Springer, Boston (2009). https:\/\/doi.org\/10.1007\/978-0-387-73003-5"},{"issue":"6","key":"55_CR21","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.1016\/j.scient.2011.08.025","volume":"18","author":"ML Shahreza","year":"2011","unstructured":"Shahreza, M.L., Moazzami, D., Moshiri, B., Delavar, M.: Anomaly detection using a self-organizing map and particle swarm optimization. Scientia Iranica 18(6), 1460\u20131468 (2011). https:\/\/doi.org\/10.1016\/j.scient.2011.08.025","journal-title":"Scientia Iranica"},{"unstructured":"Zhang, R., Zhang, S., Muthuraman, S., Jiang, J.: One class support vector machine for anomaly detection in the communication network performance data. In: Proceedings of the 5th Conference on Applied Electromagnetics, Wireless and Optical Communications, pp. 31\u201337. ELECTROSCIENCE\u201907, World Scientific and Engineering Academy and Society (WSEAS), Stevens Point (2007)","key":"55_CR22"}],"container-title":["Lecture Notes in Computer Science","Hybrid Artificial Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-29859-3_55","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:31:27Z","timestamp":1710268287000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-29859-3_55"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030298586","9783030298593"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-29859-3_55","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"26 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Hybrid Artificial Intelligence Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Le\u00f3n","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hais2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2019.haisconference.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"134","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"64","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}