{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T14:52:36Z","timestamp":1779202356625,"version":"3.51.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,6,12]],"date-time":"2020-06-12T00:00:00Z","timestamp":1591920000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,6,12]],"date-time":"2020-06-12T00:00:00Z","timestamp":1591920000000},"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":["SN COMPUT. SCI."],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1007\/s42979-020-00213-z","type":"journal-article","created":{"date-parts":[[2020,6,12]],"date-time":"2020-06-12T17:02:39Z","timestamp":1591981359000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Class-Specific Intrusion Detection Model: Hierarchical Multi-class IDS Model"],"prefix":"10.1007","volume":"1","author":[{"given":"Alper","family":"Sar\u0131kaya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Banu G\u00fcnel","family":"K\u0131l\u0131\u00e7","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,6,12]]},"reference":[{"key":"213_CR1","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.cose.2018.02.011","volume":"76","author":"C Zhong","year":"2018","unstructured":"Zhong C, Lin T, Liu P, Yen J, Chen K. A cyber security data triage operation retrieval system. Comput Secur. 2018;76:12\u201331. https:\/\/doi.org\/10.1016\/j.cose.2018.02.011.","journal-title":"Comput Secur"},{"issue":"2","key":"213_CR2","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/COMST.2015.2494502","volume":"18","author":"AL Buczak","year":"2016","unstructured":"Buczak AL, Guven E. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutor. 2016;18(2):1153\u201376. https:\/\/doi.org\/10.1109\/COMST.2015.2494502.","journal-title":"IEEE Commun Surv Tutor"},{"issue":"1\u20133","key":"213_CR3","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s10994-014-5473-9","volume":"101","author":"F Iglesias","year":"2015","unstructured":"Iglesias F, Zseby T. Analysis of network traffic features for anomaly detection. Mach Learn. 2015;101(1\u20133):59\u201384. https:\/\/doi.org\/10.1007\/s10994-014-5473-9.","journal-title":"Mach Learn"},{"issue":"4","key":"213_CR4","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1093\/comjnl\/bxt044","volume":"57","author":"P Gogoi","year":"2014","unstructured":"Gogoi P, Bhattacharyya DK, Borah B, Kalita JK. MLH-IDS: a multi-level hybrid intrusion detection method. Comput J. 2014;57(4):602\u201323. https:\/\/doi.org\/10.1093\/comjnl\/bxt044.","journal-title":"Comput J"},{"issue":"5","key":"213_CR5","doi-asserted-by":"publisher","first-page":"5947","DOI":"10.1016\/j.eswa.2010.11.028","volume":"38","author":"V Bol\u00f3n-Canedo","year":"2011","unstructured":"Bol\u00f3n-Canedo V, S\u00e1nchez-Maro\u00f1o N, Alonso-Betanzos A. Feature selection and classification in multiple class datasets: an application to KDD Cup 99 dataset. Expert Syst Appl. 2011;38(5):5947\u201357. https:\/\/doi.org\/10.1016\/j.eswa.2010.11.028.","journal-title":"Expert Syst Appl"},{"issue":"01","key":"213_CR6","doi-asserted-by":"publisher","first-page":"1650001","DOI":"10.1142\/S0218539316500017","volume":"23","author":"MM Najafabadi","year":"2016","unstructured":"Najafabadi MM, Khoshgoftaar TM, Seliya N. Evaluating feature selection methods for network intrusion detection with Kyoto data. Int J Reliab Qual Saf Eng. 2016;23(01):1650001. https:\/\/doi.org\/10.1142\/S0218539316500017.","journal-title":"Int J Reliab Qual Saf Eng"},{"key":"213_CR7","doi-asserted-by":"publisher","unstructured":"Amor NB, Benferhat S, Elouedi Z. Naive Bayes vs decision trees in intrusion detection systems. In: Proceedings of the 2004 ACM symposium on applied computing\u2014SAC\u201904. New York: ACM Press; 2004. p. 420. https:\/\/doi.org\/10.1145\/967900.967989.","DOI":"10.1145\/967900.967989"},{"key":"213_CR8","doi-asserted-by":"publisher","unstructured":"Khor K-C, Ting C-Y, Amnuaisuk S-P (2009) A feature selection approach for network intrusion detection. In: 2009 international conference on information management and engineering. p. 133\u20137. https:\/\/doi.org\/10.1109\/ICIME.2009.68.","DOI":"10.1109\/ICIME.2009.68"},{"key":"213_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2008.923876","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). 2008. https:\/\/doi.org\/10.1109\/TSMCC.2008.923876.","journal-title":"IEEE Trans Syst Man Cybern Part C (Appl Rev)"},{"key":"213_CR10","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.ins.2017.06.007","volume":"414","author":"AA Aburomman","year":"2017","unstructured":"Aburomman AA, Ibne Reaz MB. A novel weighted support vector machines multiclass classifier based on differential evolution for intrusion detection systems. Inf Sci. 2017;414:225\u201346. https:\/\/doi.org\/10.1016\/j.ins.2017.06.007.","journal-title":"Inf Sci"},{"key":"213_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2012\/850259","volume":"2012","author":"S Ganapathy","year":"2012","unstructured":"Ganapathy S, Yogesh P, Kannan A. Intelligent agent-based intrusion detection system using enhanced multiclass SVM. Comput Intell Neurosci. 2012;2012:1\u201310. https:\/\/doi.org\/10.1155\/2012\/850259.","journal-title":"Comput Intell Neurosci"},{"key":"213_CR12","doi-asserted-by":"publisher","unstructured":"Hadjadji B, Chibani Y, Guerbai Y (2014) Multiple one-class classifier combination for multi-class classification. In: 2014 22nd international conference on pattern recognition. IEEE. p. 2832\u20137. https:\/\/doi.org\/10.1109\/ICPR.2014.488.","DOI":"10.1109\/ICPR.2014.488"},{"issue":"2","key":"213_CR13","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/s10044-006-0053-7","volume":"10","author":"L Pietro Cordella","year":"2007","unstructured":"Cordella L Pietro, Sansone C. A multi-stage classification system for detecting intrusions in computer networks. Pattern Anal Appl. 2007;10(2):83\u2013100. https:\/\/doi.org\/10.1007\/s10044-006-0053-7.","journal-title":"Pattern Anal Appl"},{"issue":"5","key":"213_CR14","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1109\/TSMCC.2010.2048428","volume":"40","author":"M Tavallaee","year":"2010","unstructured":"Tavallaee M, Stakhanova N, Ghorbani AA. Toward credible evaluation of anomaly-based intrusion-detection methods. IEEE Trans Syst Man Cybern Part C (Appl Rev). 2010;40(5):516\u201324. https:\/\/doi.org\/10.1109\/TSMCC.2010.2048428.","journal-title":"IEEE Trans Syst Man Cybern Part C (Appl Rev)"},{"issue":"1","key":"213_CR15","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","volume":"40","author":"G Chandrashekar","year":"2014","unstructured":"Chandrashekar G, Sahin F. A survey on feature selection methods. Comput Electr Eng. 2014;40(1):16\u201328. https:\/\/doi.org\/10.1016\/j.compeleceng.2013.11.024.","journal-title":"Comput Electr Eng"},{"issue":"6","key":"213_CR16","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1016\/j.cose.2009.01.001","volume":"28","author":"Y Li","year":"2009","unstructured":"Li Y, Wang JL, Tian ZH, Lu TB, Young C. Building lightweight intrusion detection system using wrapper-based feature selection mechanisms. Comput Secur. 2009;28(6):466\u201375. https:\/\/doi.org\/10.1016\/j.cose.2009.01.001.","journal-title":"Comput Secur"},{"key":"213_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.aca.2011.07.027","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2003. https:\/\/doi.org\/10.1016\/j.aca.2011.07.027.","journal-title":"J Mach Learn Res"},{"key":"213_CR18","unstructured":"Ladha L, Deepa T. Feature selection methods and algorithms. Int J Comput Sci Eng. 2011; 3(5):1787\u201397. Retrieved from http:\/\/journals.indexcopernicus.com\/abstract.php?icid=945099."},{"key":"213_CR19","doi-asserted-by":"publisher","unstructured":"Jungsuk Song A, Hiroki Takakura A, Yasuo Okabe A, Masashi Eto A, Daisuke Inoue A, Koji Nakao A. Statistical analysis of honeypot data and building of Kyoto 2006+ dataset for NIDS evaluation. In: Building analysis datasets and gathering experience returns for security, vol 29. 2011. https:\/\/doi.org\/10.1145\/1978672.1978676","DOI":"10.1145\/1978672.1978676"},{"key":"213_CR20","doi-asserted-by":"publisher","unstructured":"Moustafa N, Slay J. UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: 2015 military communications and information systems conference (MilCIS). IEEE; 2015. p. 1\u20136. https:\/\/doi.org\/10.1109\/MilCIS.2015.7348942.","DOI":"10.1109\/MilCIS.2015.7348942"},{"issue":"1\u20133","key":"213_CR21","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 Glob Perspect. 2016;25(1\u20133):18\u201331. https:\/\/doi.org\/10.1080\/19393555.2015.1125974.","journal-title":"Inf Secur J Glob Perspect"},{"key":"213_CR22","doi-asserted-by":"publisher","unstructured":"Moustafa N, Slay J. A hybrid feature selection for network intrusion detection systems: central points. In: Australian information warfare and security conference, symposia and campus events. 2017. p. 5\u201313. https:\/\/doi.org\/10.4225\/75\/57a84d4fbefbb.","DOI":"10.4225\/75\/57a84d4fbefbb"},{"key":"213_CR23","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.jnca.2017.03.018","volume":"87","author":"W Haider","year":"2017","unstructured":"Haider W, Hu J, Slay J, Turnbull BP, Xie Y. Generating realistic intrusion detection system dataset based on fuzzy qualitative modeling. J Netw Comput Appl. 2017;87:185\u201392. https:\/\/doi.org\/10.1016\/j.jnca.2017.03.018.","journal-title":"J Netw Comput Appl"},{"issue":"3","key":"213_CR24","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1007\/s13042-015-0469-8","volume":"8","author":"G Aldehim","year":"2017","unstructured":"Aldehim G, Wang W. Determining appropriate approaches for using data in feature selection. Int J Mach Learn Cybern. 2017;8(3):915\u201328. https:\/\/doi.org\/10.1007\/s13042-015-0469-8.","journal-title":"Int J Mach Learn Cybern"},{"key":"213_CR25","doi-asserted-by":"publisher","unstructured":"Janarthanan T, Zargari S. Feature selection in UNSW-NB15 and KDDCUP\u201999 datasets. In: 2017 IEEE 26th international symposium on industrial electronics (ISIE). IEEE. 2017. p. 1881\u20136. https:\/\/doi.org\/10.1109\/ISIE.2017.8001537.","DOI":"10.1109\/ISIE.2017.8001537"},{"key":"213_CR26","doi-asserted-by":"publisher","first-page":"012015","DOI":"10.1088\/1742-6596\/1018\/1\/012015","volume":"1018","author":"M Nawir","year":"2018","unstructured":"Nawir M, Amir A, Lynn OB, Yaakob N, Badlishah Ahmad R. Performances of machine learning algorithms for binary classification of network anomaly detection system. J Phys: Conf Ser. 2018;1018:012015. https:\/\/doi.org\/10.1088\/1742-6596\/1018\/1\/012015.","journal-title":"J Phys: Conf Ser"},{"key":"213_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2017.03.012","author":"SM Hosseini Bamakan","year":"2017","unstructured":"Hosseini Bamakan SM, Wang H, Shi Y. Ramp loss K-support vector classification-regression; a robust and sparse multi-class approach to the intrusion detection problem. Knowl-Based Syst. 2017. https:\/\/doi.org\/10.1016\/j.knosys.2017.03.012.","journal-title":"Knowl-Based Syst"},{"key":"213_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2018.02.028","author":"V Hajisalem","year":"2018","unstructured":"Hajisalem V, Babaie S. A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection. Comput Netw. 2018. https:\/\/doi.org\/10.1016\/j.comnet.2018.02.028.","journal-title":"Comput Netw"},{"key":"213_CR29","doi-asserted-by":"publisher","DOI":"10.1109\/tbdata.2017.2715166","author":"N Moustafa","year":"2017","unstructured":"Moustafa N, Slay J, Creech G. Novel geometric area analysis technique for anomaly detection using trapezoidal area estimation on large-scale networks. IEEE Trans Big Data. 2017. https:\/\/doi.org\/10.1109\/tbdata.2017.2715166.","journal-title":"IEEE Trans Big Data"},{"key":"213_CR30","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.cose.2017.06.005","volume":"70","author":"C Khammassi","year":"2017","unstructured":"Khammassi C, Krichen S. A GA-LR wrapper approach for feature selection in network intrusion detection. Comput Secur. 2017;70:255\u201377. https:\/\/doi.org\/10.1016\/j.cose.2017.06.005.","journal-title":"Comput Secur"},{"key":"213_CR31","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1016\/j.future.2017.09.056","volume":"79","author":"D Papamartzivanos","year":"2018","unstructured":"Papamartzivanos D, G\u00f3mez M\u00e1rmol F, Kambourakis G. Dendron: genetic trees driven rule induction for network intrusion detection systems. Future Gener Comput Syst. 2018;79:558\u201374. https:\/\/doi.org\/10.1016\/j.future.2017.09.056.","journal-title":"Future Gener Comput Syst"},{"issue":"2","key":"213_CR32","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s11235-017-0395-z","volume":"68","author":"A Boulaiche","year":"2018","unstructured":"Boulaiche A, Adi K. An auto-learning approach for network intrusion detection. Telecommun Syst. 2018;68(2):277\u201394. https:\/\/doi.org\/10.1007\/s11235-017-0395-z.","journal-title":"Telecommun Syst"},{"key":"213_CR33","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, M\u00fcller A, Nothman J, Louppe G, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay \u00c9. Scikit-learn: machine learning in Python. 2012."}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-020-00213-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-020-00213-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-020-00213-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,11]],"date-time":"2021-06-11T23:16:50Z","timestamp":1623453410000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-020-00213-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,12]]},"references-count":33,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,7]]}},"alternative-id":["213"],"URL":"https:\/\/doi.org\/10.1007\/s42979-020-00213-z","relation":{},"ISSN":["2662-995X","2661-8907"],"issn-type":[{"value":"2662-995X","type":"print"},{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,12]]},"assertion":[{"value":"18 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 June 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"202"}}