{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T11:07:03Z","timestamp":1772104023740,"version":"3.50.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319395548","type":"print"},{"value":"9783319395555","type":"electronic"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-39555-5_12","type":"book-chapter","created":{"date-parts":[[2016,6,8]],"date-time":"2016-06-08T14:11:31Z","timestamp":1465395091000},"page":"212-227","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Network Anomaly Detection Using Unsupervised Feature Selection and Density Peak Clustering"],"prefix":"10.1007","author":[{"given":"Xiejun","family":"Ni","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daojing","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sammy","family":"Chan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Farooq","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,6,9]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Heady, R., Luger, G.F., Maccabe, A., et al.: The architecture of a network level intrusion detection system. Department of Computer Science, College of Engineering, University of New Mexico (1990)","DOI":"10.2172\/425295"},{"key":"12_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-0953-0","volume-title":"Applications of Data Mining in Computer Security","author":"D Barbara","year":"2002","unstructured":"Barbara, D., Jajodia, S.: Applications of Data Mining in Computer Security. Springer Science & Business Media, New York (2002)"},{"key":"12_CR3","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/978-1-4615-0953-0_4","volume-title":"Applications of Data Mining in Computer Security","author":"E Eskin","year":"2002","unstructured":"Eskin, E., Arnold, A., Prerau, M., et al.: A geometric framework for unsupervised anomaly detection. In: Barbar\u00e1, D., Jajodia, S. (eds.) Applications of Data Mining in Computer Security, pp. 77\u2013101. Springer, New York (2002)"},{"issue":"1","key":"12_CR4","first-page":"229","volume":"99","author":"M Roesch","year":"1999","unstructured":"Roesch, M.: Snort: lightweight intrusion detection for networks. LISA 99(1), 229\u2013238 (1999)","journal-title":"LISA"},{"key":"12_CR5","doi-asserted-by":"crossref","unstructured":"Camacho, J, Macia-Fernandez, G, Diaz-Verdejo, J., et al.: Tackling the big data 4 vs for anomaly detection. In: 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 500\u2013505. IEEE (2014)","DOI":"10.1109\/INFCOMW.2014.6849282"},{"issue":"12","key":"12_CR6","doi-asserted-by":"publisher","first-page":"3448","DOI":"10.1016\/j.comnet.2007.02.001","volume":"51","author":"A Patcha","year":"2007","unstructured":"Patcha, A., Park, J.M.: An overview of anomaly detection techniques: existing solutions and latest technological trends. Comput. Netw. 51(12), 3448\u20133470 (2007)","journal-title":"Comput. Netw."},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"Luo, Y.B., Wang, B.S., Sun, Y.P., et al.: FL-LPVG: an approach for anomaly detection based on flow-level limited penetrable visibility graph (2013)","DOI":"10.1049\/cp.2013.2470"},{"key":"12_CR8","unstructured":"Tran, Q.A., Duan, H., Li, X.: One-class support vector machine for anomaly network traffic detection. China Education and Research Network (CERNET), Tsinghua University, Main Building, vol. 310 (2004)"},{"key":"12_CR9","unstructured":"Hu, W., Hu, W.: Network-based intrusion detection using Adaboost algorithm. In: The 2005 IEEE\/WIC\/ACM International Conference on Web Intelligence, Proceedings, pp. 712\u2013717. IEEE (2005)"},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Zhou, Q, Gu, L, Wang, C., et al.: Using an improved C4.5 for imbalanced dataset of intrusion. In: Proceedings of the 2006 International Conference on Privacy, Security, Trust: Bridge the Gap Between PST Technologies and Business Services, p. 67. ACM (2006)","DOI":"10.1145\/1501434.1501513"},{"issue":"5","key":"12_CR11","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1109\/TSMCC.2008.923876","volume":"38","author":"J Zhang","year":"2008","unstructured":"Zhang, J., Zulkernine, M., Haque, A.: Random-forests-based network intrusion detection systems. IEEE Trans. Syst. Man Cybern Part C Appl. Rev. 38(5), 649\u2013659 (2008)","journal-title":"IEEE Trans. Syst. Man Cybern Part C Appl. Rev."},{"issue":"10","key":"12_CR12","doi-asserted-by":"publisher","first-page":"1795","DOI":"10.1016\/j.cpc.2009.05.004","volume":"180","author":"X Tong","year":"2009","unstructured":"Tong, X., Wang, Z., Yu, H.: A research using hybrid RBF\/Elman neural networks for intrusion detection system secure model. Comput. Phys. Commun. 180(10), 1795\u20131801 (2009)","journal-title":"Comput. Phys. Commun."},{"key":"12_CR13","volume-title":"Principles of Data Mining","author":"DJ Hand","year":"2001","unstructured":"Hand, D.J., Mannila, H., Smyth, P.: Principles of Data Mining. MIT Press, Cambridge (2001)"},{"key":"12_CR14","unstructured":"Leung, K., Leckie, C.: Unsupervised anomaly detection in network intrusion detection using clusters. In: Proceedings of the Twenty-Eighth Australasian Conference on Computer Science, vol. 38, pp. 333\u2013342. Australian Computer Society Inc (2005)"},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zulkernine, M.: Anomaly based network intrusion detection with unsupervised outlier detection. In: 2006 IEEE International Conference on Communications, ICC 2006, vol. 5, pp. 2388\u20132393. IEEE (2006)","DOI":"10.1109\/ICC.2006.255127"},{"key":"12_CR16","doi-asserted-by":"crossref","unstructured":"Egilmez, H.E., Ortega, A.: Spectral anomaly detection using graph-based filtering for wireless sensor networks. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1085\u20131089. IEEE (2014)","DOI":"10.1109\/ICASSP.2014.6853764"},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"Jianliang, M., Haikun, S., Ling B.: The application on intrusion detection based on k-means cluster algorithm. In: 2009 International Forum on Information Technology and Applications, IFITA 2009, vol. 1, pp. 150\u2013152. IEEE (2009)","DOI":"10.1109\/IFITA.2009.34"},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Jiang, W., Yao, M., Yan, J.: Intrusion detection based on improved fuzzy c-means algorithm. In: 2008 International Symposium on Information Science and Engineering, ISISE 2008, vol. 2, pp. 326\u2013329. IEEE (2008)","DOI":"10.1109\/ISISE.2008.17"},{"issue":"7","key":"12_CR19","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1016\/S0167-4048(03)00710-7","volume":"22","author":"SH Oh","year":"2003","unstructured":"Oh, S.H., Lee, W.S.: An anomaly intrusion detection method by clustering normal user behavior. Comput. Secur. 22(7), 596\u2013612 (2003)","journal-title":"Comput. Secur."},{"key":"12_CR20","doi-asserted-by":"crossref","unstructured":"Huang, S.Y., Huang, Y.N.: Network traffic anomaly detection based on growing hierarchical SOM. In: 2013 43rd Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN), pp. 1\u20132. IEEE (2013)","DOI":"10.1109\/DSN.2013.6575338"},{"issue":"1","key":"12_CR21","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","volume":"2","author":"S Wold","year":"1987","unstructured":"Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemometr. Intell. Lab. Syst. 2(1), 37\u201352 (1987)","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"12_CR22","doi-asserted-by":"publisher","first-page":"2067","DOI":"10.1016\/S0031-3203(00)00162-X","volume":"34","author":"H Yu","year":"2001","unstructured":"Yu, H., Yang, J.: A direct LDA algorithm for high-dimensional data with application to face recognition. Pattern Recogn. 34, 2067\u20132070 (2001)","journal-title":"Pattern Recogn."},{"issue":"8","key":"12_CR23","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226\u20131238 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"12_CR24","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1109\/TKDE.2005.136","volume":"17","author":"G Qu","year":"2005","unstructured":"Qu, G., Hariri, S., Yousif, M.: A new dependency and correlation analysis for features. IEEE Trans. Knowl. Data Eng. 17(9), 1199\u20131207 (2005)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"12_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TKDE.2011.181","volume":"25","author":"Q Song","year":"2013","unstructured":"Song, Q., Ni, J., Wang, G.: A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Trans. Knowl. Data Eng. 25(1), 1\u201314 (2013)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"12_CR26","doi-asserted-by":"crossref","unstructured":"Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: Machine Learning: Proceedings of the Twelfth International Conference, vol. 12, pp. 194\u2013202 (1995)","DOI":"10.1016\/B978-1-55860-377-6.50032-3"},{"issue":"12","key":"12_CR27","doi-asserted-by":"publisher","first-page":"1667","DOI":"10.1109\/TPAMI.2002.1114861","volume":"24","author":"N Kwak","year":"2002","unstructured":"Kwak, N., Choi, C.H.: Input feature selection by mutual information based on Parzen window. IEEE Trans. Pattern Anal. Mach. Intell. 24(12), 1667\u20131671 (2002)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"12_CR28","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1109\/34.990133","volume":"24","author":"P Mitra","year":"2002","unstructured":"Mitra, P., Murthy, C.A., Pal, S.K.: Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 301\u2013312 (2002)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6062","key":"12_CR29","doi-asserted-by":"publisher","first-page":"1518","DOI":"10.1126\/science.1205438","volume":"334","author":"DN Reshef","year":"2011","unstructured":"Reshef, D.N., Reshef, Y.A., Finucane, H.K., et al.: Detecting novel associations in large data sets. Science 334(6062), 1518\u20131524 (2011)","journal-title":"Science"},{"issue":"6191","key":"12_CR30","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1126\/science.1242072","volume":"344","author":"A Rodriguez","year":"2014","unstructured":"Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492\u20131496 (2014)","journal-title":"Science"},{"key":"12_CR31","unstructured":"Cup, K.: Data. knowledge discovery in databases darpa archive (1999)"},{"key":"12_CR32","unstructured":"Albanese, D., Filosi, M.: Mine tool. https:\/\/github.com\/minepy\/minepy"},{"key":"12_CR33","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","Applied Cryptography and Network Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-39555-5_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T21:03:22Z","timestamp":1748984602000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-39555-5_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319395548","9783319395555"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-39555-5_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016]]},"assertion":[{"value":"9 June 2016","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}