{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T06:19:45Z","timestamp":1771913985654,"version":"3.50.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030578046","type":"print"},{"value":"9783030578053","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,8,28]],"date-time":"2020-08-28T00:00:00Z","timestamp":1598572800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,28]],"date-time":"2020-08-28T00:00:00Z","timestamp":1598572800000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-57805-3_38","type":"book-chapter","created":{"date-parts":[[2020,8,27]],"date-time":"2020-08-27T13:04:15Z","timestamp":1598533455000},"page":"405-414","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Hybrid Model for Improving the Classification Effectiveness of Network Intrusion Detection"],"prefix":"10.1007","author":[{"given":"Vibekananda","family":"Dutta","sequence":"first","affiliation":[]},{"given":"Micha\u0142","family":"Chora\u015b","sequence":"additional","affiliation":[]},{"given":"Rafa\u0142","family":"Kozik","sequence":"additional","affiliation":[]},{"given":"Marek","family":"Pawlicki","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,28]]},"reference":[{"key":"38_CR1","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.cose.2016.11.004","volume":"65","author":"AA Aburomman","year":"2017","unstructured":"Aburomman, A.A., Reaz, M.B.I.: A survey of intrusion detection systems based on ensemble and hybrid classifiers. Comput. Secur. 65, 135\u2013152 (2017)","journal-title":"Comput. Secur."},{"key":"38_CR2","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.jocs.2017.03.006","volume":"25","author":"S Aljawarneh","year":"2018","unstructured":"Aljawarneh, S., Aldwairi, M., Yassein, M.B.: Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. J. Comput. Sci. 25, 152\u2013160 (2018)","journal-title":"J. Comput. Sci."},{"issue":"1","key":"38_CR3","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1093\/jigpal\/jzu038","volume":"23","author":"M Chora\u015b","year":"2015","unstructured":"Chora\u015b, M., Kozik, R.: Machine learning techniques applied to detect cyber attacks on web applications. Logic J. IGPL 23(1), 45\u201356 (2015)","journal-title":"Logic J. IGPL"},{"issue":"6","key":"38_CR4","first-page":"446","volume":"4","author":"L Dhanabal","year":"2015","unstructured":"Dhanabal, L., Shantharajah, S.P.: A study on nsl-kdd dataset for intrusion detection system based on classification algorithms. Int. J. Adv. Res. Comput. Commun. Eng. 4(6), 446\u2013452 (2015)","journal-title":"Int. J. Adv. Res. Comput. Commun. Eng."},{"key":"38_CR5","doi-asserted-by":"publisher","first-page":"10015","DOI":"10.1109\/ACCESS.2019.2891933","volume":"7","author":"Y Djenouri","year":"2019","unstructured":"Djenouri, Y., Belhadi, A., Lin, J.C.-W., Cano, A.: Adapted k-nearest neighbors for detecting anomalies on spatio-temporal traffic flow. IEEE Access 7, 10015\u201310027 (2019)","journal-title":"IEEE Access"},{"issue":"6","key":"38_CR6","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1080\/0952813X.2018.1509379","volume":"30","author":"R Ganeshan","year":"2018","unstructured":"Ganeshan, R., Rodrigues, S.P.: I-AHSDT: intrusion detection using adaptive dynamic directive operative fractional lion clustering and hyperbolic secant-based decision tree classifier. J. Exp. Theoret. Artif. Intell. 30(6), 887\u2013910 (2018)","journal-title":"J. Exp. Theoret. Artif. Intell."},{"issue":"1","key":"38_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/1869-0238-4-5","volume":"4","author":"K Hashizume","year":"2013","unstructured":"Hashizume, K., Rosado, D.G., Fern\u00e1ndez-Medina, E., Fernandez, E.B.: An analysis of security issues for cloud computing. J. Internet Serv. Appl. 4(1), 5 (2013)","journal-title":"J. Internet Serv. Appl."},{"issue":"7","key":"38_CR8","first-page":"1372","volume":"5","author":"A Jain","year":"2014","unstructured":"Jain, A., Verma, B., Rana, J.L.: Anomaly intrusion detection techniques: a brief review. Int. J. Sci. Eng. Res. 5(7), 1372\u20131383 (2014)","journal-title":"Int. J. Sci. Eng. Res."},{"key":"38_CR9","unstructured":"Jidiga, G.R., Sammulal, P.: Anomaly detection using machine learning with a case study. In: 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, pp. 1060\u20131065. IEEE (2014)"},{"key":"38_CR10","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.eswa.2018.04.038","volume":"108","author":"A Karami","year":"2018","unstructured":"Karami, A.: An anomaly-based intrusion detection system in presence of benign outliers with visualization capabilities. Expert Syst. Appl. 108, 36\u201360 (2018)","journal-title":"Expert Syst. Appl."},{"key":"38_CR11","unstructured":"Kayacik, H.G., Zincir-Heywood, A.N., Heywood, M.I.: Selecting features for intrusion detection: a feature relevance analysis on KDD 99 intrusion detection datasets. In: Proceedings of the Third Annual Conference on Privacy, Security and Trust, vol.\u00a094, pp. 1723\u20131722 (2005)"},{"key":"38_CR12","unstructured":"Kingma, D.P., Adam, J.B.: A method for stochastic optimization. arXiv preprint \narXiv:1412.6980\n\n (2014)"},{"key":"38_CR13","doi-asserted-by":"crossref","unstructured":"Kozik, R., Chora\u015b, M.: Current cyber security threats and challenges in critical infrastructures protection. In: 2013 Second International Conference on Informatics & Applications (ICIA), pp. 93\u201397. IEEE (2013)","DOI":"10.1109\/ICoIA.2013.6650236"},{"issue":"2","key":"38_CR14","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1093\/jigpal\/jzy029","volume":"27","author":"R Kozik","year":"2019","unstructured":"Kozik, R., Chora\u015b, M.: Protecting the application layer in the public domain with machine learning methods. Logic J. IGPL 27(2), 149\u2013159 (2019)","journal-title":"Logic J. IGPL"},{"issue":"3","key":"38_CR15","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/MPRV.2018.03367731","volume":"17","author":"Y Meidan","year":"2018","unstructured":"Meidan, Y., et al.: N-baiot\u2013network-based detection of iot botnet attacks using deep autoencoders. IEEE Pervasive Comput. 17(3), 12\u201322 (2018)","journal-title":"IEEE Pervasive Comput."},{"issue":"1\u20133","key":"38_CR16","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1080\/19393555.2015.1125974","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. Global Perspect. 25(1\u20133), 18\u201331 (2016)","journal-title":"Inf. Secur. J. Global Perspect."},{"issue":"3.24","key":"38_CR17","first-page":"479","volume":"7","author":"R Panigrahi","year":"2018","unstructured":"Panigrahi, R., Borah, S.: A detailed analysis of CICIDS2017 dataset for designing intrusion detection systems. Int. J. Eng. Technol. 7(3.24), 479\u2013482 (2018)","journal-title":"Int. J. Eng. Technol."},{"key":"38_CR18","first-page":"11","volume":"2019","author":"J Ren","year":"2019","unstructured":"Ren, J., Guo, J., Qian, W., Yuan, H., Hao, X., Jingjing, H.: Building an effective intrusion detection system by using hybrid data optimization based on machine learning algorithms. Secur. Commun. Netw. 2019, 11 (2019)","journal-title":"Secur. Commun. Netw."},{"key":"38_CR19","doi-asserted-by":"crossref","unstructured":"Shang, W., Cui, J., Song, C., Zhao, J., Zeng, P.: Research on industrial control anomaly detection based on FCM and SVM. In: 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications\/12th IEEE International Conference on Big Data Science and Engineering (TrustCom\/BigDataSE), pp. 218\u2013222. IEEE (2018)","DOI":"10.1109\/TrustCom\/BigDataSE.2018.00042"},{"key":"38_CR20","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.neucom.2018.05.027","volume":"310","author":"Y Tian","year":"2018","unstructured":"Tian, Y., Mirzabagheri, M., Bamakan, S.M.H., Wang, H., Qiang, Q.: Ramp loss one-class support vector machine; a robust and effective approach to anomaly detection problems. Neurocomputing 310, 223\u2013235 (2018)","journal-title":"Neurocomputing"},{"issue":"1","key":"38_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TVCG.2017.2744878","volume":"24","author":"K Wongsuphasawat","year":"2017","unstructured":"Wongsuphasawat, K., et al.: Visualizing dataflow graphs of deep learning models in tensorflow. IEEE Trans. Vis. Comput. Graph. 24(1), 1\u201312 (2017)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"38_CR22","doi-asserted-by":"publisher","first-page":"35365","DOI":"10.1109\/ACCESS.2018.2836950","volume":"6","author":"Y Xin","year":"2018","unstructured":"Xin, Y., et al.: Machine learning and deep learning methods for cybersecurity. IEEE Access 6, 35365\u201335381 (2018)","journal-title":"IEEE Access"},{"issue":"11","key":"38_CR23","doi-asserted-by":"publisher","first-page":"2528","DOI":"10.3390\/s19112528","volume":"19","author":"Y Yang","year":"2019","unstructured":"Yang, Y., Zheng, K., Chunhua, W., Yang, Y.: Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network. Sensors 19(11), 2528 (2019)","journal-title":"Sensors"},{"key":"38_CR24","unstructured":"Zhou, Y., Arpit, D., Nwogu, I., Govindaraju, V.: Is joint training better for deep auto-encoders? arXiv preprint \narXiv:1405.1380\n\n (2014)"}],"container-title":["Advances in Intelligent Systems and Computing","13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020)"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-57805-3_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,7]],"date-time":"2020-09-07T11:14:55Z","timestamp":1599477295000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-57805-3_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,28]]},"ISBN":["9783030578046","9783030578053"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-57805-3_38","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"value":"2194-5357","type":"print"},{"value":"2194-5365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,28]]},"assertion":[{"value":"28 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}