{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:01:00Z","timestamp":1743141660411,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030608019"},{"type":"electronic","value":"9783030608026"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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":[[2020]]},"DOI":"10.1007\/978-3-030-60802-6_53","type":"book-chapter","created":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T14:04:35Z","timestamp":1602597875000},"page":"607-618","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Ensemble Classification Technique for Intrusion Detection Based on Dual Evolution"],"prefix":"10.1007","author":[{"given":"Qiuzhen","family":"Lin","sequence":"first","affiliation":[]},{"given":"Chao","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Shuo","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Peizhi","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,5]]},"reference":[{"issue":"3","key":"53_CR1","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1109\/32.372146","volume":"21","author":"K Ilgun","year":"1995","unstructured":"Ilgun, K., Kemmerer, R.A., Porras, P.A.: State transition analysis: a rule-based intrusion detection system. IEEE Trans. Softw. Eng. 21(3), 181\u2013199 (1995)","journal-title":"IEEE Trans. Softw. Eng."},{"key":"53_CR2","doi-asserted-by":"crossref","unstructured":"Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, pp. 1975\u20131981 (2010)","DOI":"10.1109\/CVPR.2010.5539872"},{"issue":"4","key":"53_CR3","doi-asserted-by":"publisher","first-page":"1617","DOI":"10.1109\/TCYB.2018.2877663","volume":"50","author":"Z Zhu","year":"2020","unstructured":"Zhu, Z., Wang, Z., Li, D., Zhu, Y., Du, W.: Geometric structural ensemble learning for imbalanced problems. IEEE Trans. Cybern. 50(4), 1617\u20131629 (2020)","journal-title":"IEEE Trans. Cybern."},{"key":"53_CR4","unstructured":"Arafat, M., Jain, A., Wu, Y.: Analysis of intrusion detection dataset NSL-KDD using KNIME analytics. In: 13th International Conference on Cyber Warfare and Security, Washington, pp. 573\u2013583 (2018)"},{"key":"53_CR5","doi-asserted-by":"crossref","unstructured":"Wang, S., Yao, X.: Diversity analysis on imbalanced data sets by using ensemble models. In: 2009 IEEE Symposium on Computational Intelligence and Data Mining, Nashville, TN, pp. 324\u2013331 (2009)","DOI":"10.1109\/CIDM.2009.4938667"},{"key":"53_CR6","doi-asserted-by":"crossref","unstructured":"Saglam, F., Cengiz, M.A.: Noise detection in imbalanced classes using adaptive boosting. In: 2019 4th International Conference on Computer Science and Engineering (UBMK), Samsun, Turkey, pp. 449\u2013452 (2019)","DOI":"10.1109\/UBMK.2019.8907017"},{"issue":"9","key":"53_CR7","doi-asserted-by":"publisher","first-page":"2850","DOI":"10.1109\/TCYB.2016.2579658","volume":"47","author":"P Lim","year":"2017","unstructured":"Lim, P., Goh, C.K., Tan, K.C.: Evolutionary cluster-based synthetic oversampling ensemble (ECO-ensemble) for imbalance learning. IEEE Trans. Cybern. 47(9), 2850\u20132861 (2017)","journal-title":"IEEE Trans. Cybern."},{"key":"53_CR8","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Self-paced ensemble for highly imbalanced massive data classification. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), Dallas, TX, USA, pp. 841\u2013852 (2020)","DOI":"10.1109\/ICDE48307.2020.00078"},{"key":"53_CR9","doi-asserted-by":"crossref","unstructured":"Alipourfard, T., Arefi, H., Mahmoudi, S.: A novel deep learning framework by combination of subspace-based feature extraction and convolutional neural networks for hyperspectral images classification. In: 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, pp. 4780\u20134783 (2018)","DOI":"10.1109\/IGARSS.2018.8518956"},{"key":"53_CR10","doi-asserted-by":"crossref","unstructured":"Peker, M., Arslan, A., \u015een, B., \u00c7elebi, F.V., But, A.: A novel hybrid method for determining the depth of anesthesia level: combining ReliefF feature selection and random forest algorithm (ReliefF+RF). In: 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), Madrid, pp. 1\u20138 (2015)","DOI":"10.1109\/INISTA.2015.7276737"},{"key":"53_CR11","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.comnet.2018.11.010","volume":"148","author":"F Salo","year":"2019","unstructured":"Salo, F., Nassif, A.B., Essex, A.: Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection. Comput. Netw. 148, 164\u2013175 (2019)","journal-title":"Comput. Netw."},{"issue":"10","key":"53_CR12","doi-asserted-by":"publisher","first-page":"1768","DOI":"10.1109\/TNN.2008.2002078","volume":"19","author":"S Ji","year":"2008","unstructured":"Ji, S., Ye, J.: Generalized linear discriminant analysis: a unified framework and efficient model selection. IEEE Trans. Neural Netw. 19(10), 1768\u20131782 (2008)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"1","key":"53_CR13","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/TETCI.2017.2772792","volume":"2","author":"N Shone","year":"2018","unstructured":"Shone, N., Ngoc, T.N., Phai, V.D., Shi, Q.: A deep learning approach to network intrusion detection. IEEE Trans. Emerg. Top. Comput. Intell. 2(1), 41\u201350 (2018)","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"53_CR14","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.knosys.2016.10.030","volume":"116","author":"Y Zhu","year":"2017","unstructured":"Zhu, Y., Liang, J., Chen, J., Ming, Z.: An improved NSGA-III algorithm for feature selection used in intrusion detection. Knowl.-Based Syst. 116, 74\u201385 (2017)","journal-title":"Knowl.-Based Syst."},{"issue":"5","key":"53_CR15","first-page":"1216","volume":"37","author":"YH Yang","year":"2014","unstructured":"Yang, Y.H., Huang, H.Z., Shen, Q.N., Wu, Z.H., Zhang, Y.: Research on intrusion detection based on incremental GHSOM. Chin. J. Comput. 37(5), 1216\u20131224 (2014)","journal-title":"Chin. J. Comput."},{"key":"53_CR16","doi-asserted-by":"crossref","unstructured":"Hui, H.W., Woo, Y.H., Hung, C.H., Leung, K.N.: A double gain-boosted amplifier with widened output swing based on signal-and transient-current boosting technique in CMOS 130-nm technology. In: 2018 IEEE International Conference on Electron Devices and Solid State Circuits (EDSSC), Shenzhen, pp. 1\u20132 (2018)","DOI":"10.1109\/EDSSC.2018.8487164"},{"key":"53_CR17","doi-asserted-by":"crossref","unstructured":"Yu, Y., Su, H.: Collaborative representation ensemble using bagging for hyperspectral image classification. In: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, pp. 2738\u20132741 (2019)","DOI":"10.1109\/IGARSS.2019.8898684"},{"issue":"2","key":"53_CR18","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.A.M.T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182\u2013197 (2002)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"53_CR19","doi-asserted-by":"crossref","unstructured":"Gupta, A.K., Sardana, N.: Significance of clustering coefficient over jaccard index. In: 2015 Eighth International Conference on Contemporary Computing (IC3), Noida, pp. 463\u2013466 (2015)","DOI":"10.1109\/IC3.2015.7346726"},{"issue":"4","key":"53_CR20","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/TEVC.2013.2281535","volume":"18","author":"K Deb","year":"2014","unstructured":"Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577\u2013601 (2014)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"1","key":"53_CR21","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1109\/COMST.2015.2402161","volume":"18","author":"C Kolias","year":"2016","unstructured":"Kolias, C., Kambourakis, G., Stavrou, A., Gritzalis, S.: Intrusion detection in 80211 networks: empirical evaluation of threats and a public dataset. IEEE Commun. Surv. Tutor. 18(1), 184\u2013208 (2016)","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"53_CR22","doi-asserted-by":"crossref","unstructured":"Abdulhammed, R., Faezipour, M., Abuzneid, A., Alessa, A.: Effective features selection and machine learning classifiers for improved wireless intrusion detection. In: 2018 International Symposium on Networks, Computers and Communications (ISNCC), Rome, pp. 1\u20136 (2018)","DOI":"10.1109\/ISNCC.2018.8530969"}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Theories and Application"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60802-6_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T14:40:14Z","timestamp":1602600014000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-60802-6_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030608019","9783030608026"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60802-6_53","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"5 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bari","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ic-ic.tongji.edu.cn\/2020\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}