{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T16:02:48Z","timestamp":1770912168024,"version":"3.50.1"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T00:00:00Z","timestamp":1683244800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T00:00:00Z","timestamp":1683244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"ICT Division, Ministry of Posts, Telecommunications and Information Technology, Bangladesh","award":["56.00.0000.028.33.005.20-120"],"award-info":[{"award-number":["56.00.0000.028.33.005.20-120"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Inf. Secur."],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s10207-023-00694-y","type":"journal-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T10:02:16Z","timestamp":1683280936000},"page":"1355-1369","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["cFEM: a cluster based feature extraction method for network intrusion detection"],"prefix":"10.1007","volume":"22","author":[{"given":"Md. Mumtahin Habib Ullah","family":"Mazumder","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md. Eusha","family":"Kadir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5783-6242","authenticated-orcid":false,"given":"Sadia","family":"Sharmin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md. Shariful","family":"Islam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Mahbub","family":"Alam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,5]]},"reference":[{"issue":"2","key":"694_CR1","first-page":"48","volume":"16","author":"M Abdullah","year":"2018","unstructured":"Abdullah, M., et al.: Enhanced intrusion detection system using feature selection method and ensemble learning algorithms. IJCSIS 16(2), 48\u201355 (2018)","journal-title":"IJCSIS"},{"key":"694_CR2","doi-asserted-by":"crossref","unstructured":"Aburomman, A.A., Reaz, M.B.I.: Ensemble of binary SVM classifiers based on PCA and LDA feature extraction for intrusion detection. In: IMCEC, pp. 636\u2013640 (2016)","DOI":"10.1109\/IMCEC.2016.7867287"},{"key":"694_CR3","doi-asserted-by":"publisher","first-page":"52843","DOI":"10.1109\/ACCESS.2018.2869577","volume":"6","author":"M Al-Qatf","year":"2018","unstructured":"Al-Qatf, M., et al.: Deep learning approach combining sparse autoencoder with SVM for network intrusion detection. IEEE Access 6, 52843\u201352856 (2018)","journal-title":"IEEE Access"},{"issue":"10","key":"694_CR4","doi-asserted-by":"publisher","first-page":"2986","DOI":"10.1109\/TC.2016.2519914","volume":"65","author":"MA Ambusaidi","year":"2016","unstructured":"Ambusaidi, M.A., et al.: Building an intrusion detection system using a filter-based feature selection algorithm. IEEE Trans. Comput. 65(10), 2986\u20132998 (2016). https:\/\/doi.org\/10.1109\/TC.2016.2519914","journal-title":"IEEE Trans. Comput."},{"issue":"2","key":"694_CR5","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s11416-014-0215-x","volume":"11","author":"C Annachhatre","year":"2015","unstructured":"Annachhatre, C., Austin, T.H., Stamp, M.: Hidden Markov models for malware classification. J. Comput. Virol. Hack. Techn. 11(2), 59\u201373 (2015)","journal-title":"J. Comput. Virol. Hack. Techn."},{"key":"694_CR6","doi-asserted-by":"publisher","unstructured":"Ashok, R., et al.: Optimized feature selection with k-means clustered triangle SVM for Intrusion Detection. In: 2011 3rd International Conference on Advanced Computing, pp. 23\u201327 (2011). https:\/\/doi.org\/10.1109\/ICoAC.2011.6165213","DOI":"10.1109\/ICoAC.2011.6165213"},{"key":"694_CR7","doi-asserted-by":"crossref","unstructured":"Ayub, M.A., et al: Model evasion attack on intrusion detection systems using adversarial machine learning. In: CISS, pp. 1\u20136. IEEE (2020)","DOI":"10.1109\/CISS48834.2020.1570617116"},{"issue":"4","key":"694_CR8","first-page":"233","volume":"12","author":"MN Aziz","year":"2019","unstructured":"Aziz, M.N., Ahmad, T.: Cluster analysis-based approach features selection on machine learning for detecting intrusion. Int. J. Intell. Eng. Syst. 12(4), 233\u2013243 (2019)","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"694_CR9","doi-asserted-by":"crossref","unstructured":"Beqiri, E.: Neural networks for intrusion detection systems. In: International Conference on Global Security, Safety, and Sustainability, pp. 156\u2013165. Springer (2009)","DOI":"10.1007\/978-3-642-04062-7_17"},{"issue":"1","key":"694_CR10","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"694_CR11","unstructured":"Chen, L.-S., Syu, J.-S.: Feature extraction based approaches for improving the performance of intrusion detection systems. In: Proceedings of the International MultiConference of Engineers and Computer Scientists, vol. 1, pp. 18\u201320 (2015)"},{"key":"694_CR12","unstructured":"Cyber Security Report. https:\/\/docs.broadcom.com\/doc\/istr-22-2017-en. Accessed (2021)"},{"key":"694_CR13","doi-asserted-by":"publisher","unstructured":"Eid, H., et al.: Linear correlation-based feature selection for network intrusion detection model. In: vol. 381. ISBN: 978-3-642- 40596-9 (2013). https:\/\/doi.org\/10.1007\/978-3-642-40597-6_21","DOI":"10.1007\/978-3-642-40597-6_21"},{"issue":"1","key":"694_CR14","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.eswa.2014.08.002","volume":"42","author":"S Elhag","year":"2015","unstructured":"Elhag, S., et al.: On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on intrusion detection systems. Expert Syst. Appl. 42(1), 193\u2013202 (2015)","journal-title":"Expert Syst. Appl."},{"key":"694_CR15","doi-asserted-by":"crossref","unstructured":"Farahnakian, F., Heikkonen, J.: A deep auto-encoder based approach for intrusion detection system. In: 2018 20th International Conference on Advanced Communication Technology (ICACT), pp. 178\u2013183. IEEE (2018)","DOI":"10.23919\/ICACT.2018.8323687"},{"key":"694_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2012.09.004","volume":"40","author":"X Gan","year":"2013","unstructured":"Gan, X., et al.: Anomaly intrusion detection based on PLS feature extraction and core vector machine. Knowl. Based Syst. 40, 1\u20136 (2013)","journal-title":"Knowl. Based Syst."},{"key":"694_CR17","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.cose.2019.02.008","volume":"83","author":"F Gottwalt","year":"2019","unstructured":"Gottwalt, F., Chang, E., Dillon, T.: CorrCorr: a feature selection method for multivariate correlation network anomaly detection techniques. Comput. Secur. 83, 234\u2013245 (2019)","journal-title":"Comput. Secur."},{"key":"694_CR18","doi-asserted-by":"crossref","unstructured":"Hota, H.S., Shrivas, A.K.: Decision tree techniques applied on NSL-KDD data and its comparison with various feature selection techniques. In: Advanced Computing, Networking and Informatics, vol. 1, pp. 205\u2013211. Springer (2014)","DOI":"10.1007\/978-3-319-07353-8_24"},{"issue":"10","key":"694_CR19","doi-asserted-by":"publisher","first-page":"2585","DOI":"10.1007\/s10115-021-01605-0","volume":"63","author":"X Hu","year":"2021","unstructured":"Hu, X., et al.: Model complexity of deep learning: a survey. Knowl. Inf. Syst. 63(10), 2585\u20132619 (2021)","journal-title":"Knowl. Inf. Syst."},{"issue":"9","key":"694_CR20","volume":"3","author":"A Javaid","year":"2016","unstructured":"Javaid, A., et al.: A deep learning approach for network intrusion detection system. Eai Endorsed Trans. Secur. Saf. 3(9), e2 (2016)","journal-title":"Eai Endorsed Trans. Secur. Saf."},{"issue":"3","key":"694_CR21","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1109\/TKDE.2010.263","volume":"24","author":"K Javed","year":"2010","unstructured":"Javed, K., Babri, H.A., Saeed, M.: Feature selection based on class-dependent densities for high-dimensional binary data. IEEE Trans. Knowl. Data Eng. 24(3), 465\u2013477 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"694_CR22","first-page":"25","volume":"3","author":"J Jha","year":"2013","unstructured":"Jha, J., Ragha, L.: Intrusion detection system using support vector machine. IJAIS 3, 25\u201330 (2013)","journal-title":"IJAIS"},{"key":"694_CR23","doi-asserted-by":"publisher","first-page":"30373","DOI":"10.1109\/ACCESS.2019.2899721","volume":"7","author":"FA Khan","year":"2019","unstructured":"Khan, F.A., et al.: A novel two-stage deep learning model for efficient network intrusion detection. IEEE Access 7, 30373\u201330385 (2019)","journal-title":"IEEE Access"},{"issue":"1","key":"694_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s42400-019-0038-7","volume":"2","author":"A Khraisat","year":"2019","unstructured":"Khraisat, A., et al.: Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity 2(1), 1\u201322 (2019)","journal-title":"Cybersecurity"},{"key":"694_CR25","doi-asserted-by":"crossref","unstructured":"Kim, J. et al.: Long short term memory recurrent neural network classifier for intrusion detection. In: International Conference on Platform Technology and Service (PlatCon), vol 2016, pp. 1\u20135. IEEE (2016)","DOI":"10.1109\/PlatCon.2016.7456805"},{"issue":"18","key":"694_CR26","doi-asserted-by":"publisher","first-page":"13492","DOI":"10.1016\/j.eswa.2012.07.009","volume":"39","author":"L Koc","year":"2012","unstructured":"Koc, L., Mazzuchi, T.A., Sarkani, S.: A network intrusion detection system based on a Hidden Na\u0131ve Bayes multiclass classifier. Expert Syst. Appl. 39(18), 13492\u201313500 (2012)","journal-title":"Expert Syst. Appl."},{"key":"694_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcde.2017.02.005","author":"M Kohli","year":"2017","unstructured":"Kohli, M., Arora, S.: Chaotic grey wolf optimization algorithm for constrained optimization problems. J. Comput. Design Eng. (2017). https:\/\/doi.org\/10.1016\/j.jcde.2017.02.005","journal-title":"J. Comput. Design Eng."},{"issue":"1","key":"694_CR28","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1145\/972374.972384","volume":"34","author":"C Kreibich","year":"2004","unstructured":"Kreibich, C., Crowcroft, J.: Honeycomb: creating intrusion detection signatures using honeypots. ACM SIGCOMM Comput. Commun. Rev. 34(1), 51\u201356 (2004)","journal-title":"ACM SIGCOMM Comput. Commun. Rev."},{"key":"694_CR29","doi-asserted-by":"crossref","unstructured":"Le, T.-T.-H., Kim, J., Kim, H., et\u00a0al.:An effective intrusion detection classifier using long short-term memory with gradient descent optimization. In: 2017 International Conference on Platform Technology and Service (Plat- Con), pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/PlatCon.2017.7883684"},{"key":"694_CR30","doi-asserted-by":"crossref","unstructured":"Li, Q., et al.: An intrusion detection system based on polynomial feature correlation analysis. In: IEEE Trustcom\/BigDataSE\/ICESS. vol. 2017, pp. 978\u2013983. IEEE (2017)","DOI":"10.1109\/Trustcom\/BigDataSE\/ICESS.2017.340"},{"issue":"1","key":"694_CR31","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.eswa.2011.07.032","volume":"39","author":"Y Li","year":"2012","unstructured":"Li, Y., et al.: An efficient intrusion detection system based on support vector machines and gradually feature removal method. Exp. Syst. Appl. 39(1), 424\u2013430 (2012)","journal-title":"Exp. Syst. Appl."},{"issue":"5","key":"694_CR32","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/S0167-4048(02)00514-X","volume":"21","author":"Y Liao","year":"2002","unstructured":"Liao, Y., Vemuri, V.R.: Use of k-nearest neighbor classifier for intrusion detection. Comput. Secur. 21(5), 439\u2013448 (2002)","journal-title":"Comput. Secur."},{"issue":"4","key":"694_CR33","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1109\/TC.2010.95","volume":"60","author":"P-C Lin","year":"2010","unstructured":"Lin, P.-C., Lin, Y.-D., Lai, Y.-C.: A hybrid algorithm of backward hashing and automaton tracking for virus scanning. IEEE Trans. Comput. 60(4), 594\u2013601 (2010)","journal-title":"IEEE Trans. Comput."},{"issue":"10","key":"694_CR34","doi-asserted-by":"publisher","first-page":"3285","DOI":"10.1016\/j.asoc.2012.05.004","volume":"12","author":"S-W Lin","year":"2012","unstructured":"Lin, S.-W., et al.: An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection. Appl. Soft Comput. 12(10), 3285\u20133290 (2012)","journal-title":"Appl. Soft Comput."},{"key":"694_CR35","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.knosys.2015.01.009","volume":"78","author":"W-C Lin","year":"2015","unstructured":"Lin, W.-C., Ke, S.-W., Tsai, C.-F.: CANN: An intrusion detection system based on combining cluster centers and nearest neighbors. Knowl.-Based Syst. 78, 13\u201321 (2015)","journal-title":"Knowl.-Based Syst."},{"issue":"20","key":"694_CR36","doi-asserted-by":"publisher","first-page":"4396","DOI":"10.3390\/app9204396","volume":"9","author":"H Liu","year":"2019","unstructured":"Liu, H., Lang, B.: Machine learning and deep learning methods for intrusion detection systems: a survey. Appl. Sci. 9(20), 4396 (2019)","journal-title":"Appl. Sci."},{"issue":"13","key":"694_CR37","doi-asserted-by":"publisher","first-page":"1818","DOI":"10.1016\/j.patrec.2007.05.018","volume":"28","author":"S Manocha","year":"2007","unstructured":"Manocha, S., Girolami, M.A.: An empirical analysis of the probabilistic K-nearest neighbour classifier. Pattern Recognit. Lett. 28(13), 1818\u20131824 (2007)","journal-title":"Pattern Recognit. Lett."},{"key":"694_CR38","doi-asserted-by":"publisher","first-page":"116216","DOI":"10.1109\/ACCESS.2020.3004699","volume":"8","author":"J Mao","year":"2020","unstructured":"Mao, J., et al.: CBFS: a clustering-based feature selection mechanism for network anomaly detection. IEEE Access 8, 116216\u2013116225 (2020)","journal-title":"IEEE Access"},{"key":"694_CR39","unstructured":"Meiners, C.R., et\u00a0al.: Fast Regular Expression Matching Using Small TCAMs for Network Intrusion Detection and Prevention Systems. In: 19th USENIX Security Symposium (USENIX Security 10).Washington, DC: USENIX Association (2010)"},{"issue":"3","key":"694_CR40","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1007\/s10207-020-00508-5","volume":"20","author":"SN Mighan","year":"2021","unstructured":"Mighan, S.N., Kahani, M.: A novel scalable intrusion detection system based on deep learning. Int. J. Inf. Secur. 20(3), 387\u2013403 (2021)","journal-title":"Int. J. Inf. Secur."},{"key":"694_CR41","doi-asserted-by":"crossref","unstructured":"Mirza, A.H., Cosan, S.: Computer network intrusion detection using sequential LSTM neural networks autoencoders. In: 26th Signalprocessing and Communications Applications Conference (SIU), vol. 2018, pp. 1\u20134. IEEE (2018)","DOI":"10.1109\/SIU.2018.8404689"},{"issue":"1","key":"694_CR42","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.jnca.2012.05.003","volume":"36","author":"C Modi","year":"2013","unstructured":"Modi, C., et al.: A survey of intrusion detection techniques in cloud. J. Netw. Comput. Appl. 36(1), 42\u201357 (2013)","journal-title":"J. Netw. Comput. Appl."},{"key":"694_CR43","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: MilCIS, pp. 1\u20136. IEEE (2015)","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"694_CR44","doi-asserted-by":"crossref","unstructured":"Mudzingwa, D., Rajeev Agrawal, A.: Study of methodologies used in intrusion detection and prevention systems (IDPS). In: Proceedings of IEEE Southeastcon, vol. 2012, pp. 1\u20136. IEEE (2012)","DOI":"10.1109\/SECon.2012.6197080"},{"key":"694_CR45","doi-asserted-by":"crossref","unstructured":"Nguyen, S.-N., et\u00a0al.: Design and implementation of intrusion detection system using convolutional neural network for DoS detection. In: Proceedings of the 2nd International Conference on Machine Learning and Soft Computing, pp. 34\u201338 (2018)","DOI":"10.1145\/3184066.3184089"},{"issue":"4","key":"694_CR46","doi-asserted-by":"publisher","first-page":"148","DOI":"10.14569\/IJACSA.2016.070419","volume":"7","author":"H Nkiama","year":"2016","unstructured":"Nkiama, H., Said, S.Z.M., Saidu, M.: A subset feature elimination mechanism for intrusion detection system. IJACSA 7(4), 148\u2013157 (2016)","journal-title":"IJACSA"},{"key":"694_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.proeng.2012.01.827","volume":"30","author":"M Panda","year":"2012","unstructured":"Panda, M., Abraham, A., Patra, M.R.: A hybrid intelligent approach for network intrusion detection. Procedia Eng. 30, 1\u20139 (2012)","journal-title":"Procedia Eng."},{"key":"694_CR48","doi-asserted-by":"crossref","unstructured":"Potluri, S., Henry, N.F., Diedrich, C.: Evaluation of hybrid deep learning techniques for ensuring security in networked control systems. In: 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) , pp. 1\u20138 (2017)","DOI":"10.1109\/ETFA.2017.8247662"},{"key":"694_CR49","doi-asserted-by":"crossref","unstructured":"Rao, K.N., Rao, K.V., Prasad Reddy, P.V.G.D.: A comprehensive survey of machine learning for intrusion detection. Int. J. Res. Advent Technol. 7:148\u2013156 (2019)","DOI":"10.32622\/ijrat.72201941"},{"key":"694_CR50","doi-asserted-by":"crossref","unstructured":"Rao, K.N., Rao, K.V., Prasad Reddy, P.V.G.D.: A hybrid intrusion detection system based on sparse autoencoder and deep neural network. Comput. Commun. 180:77\u201388 (2021)","DOI":"10.1016\/j.comcom.2021.08.026"},{"key":"694_CR51","doi-asserted-by":"crossref","unstructured":"Ravinder Reddy, R., Ramadevi, Y., Sunitha, K.V.N.: Effective discriminant function for intrusion detection using SVM. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), vol. 2016, p. 11481153. IEEE (2016)","DOI":"10.1109\/ICACCI.2016.7732199"},{"key":"694_CR52","doi-asserted-by":"crossref","unstructured":"Rezvy, S., et\u00a0al.: Intrusion detection and classification with autoencoded deep neural network. In: International Conference on Security for Information Technology and Communications, pp. 142\u2013156. Springer (2018)","DOI":"10.1007\/978-3-030-12942-2_12"},{"key":"694_CR53","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.comcom.2020.05.048","volume":"160","author":"RM Swarna Priya","year":"2020","unstructured":"Swarna Priya, R.M., et al.: An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architecture. Comput. Commun. 160, 139\u2013149 (2020)","journal-title":"Comput. Commun."},{"issue":"1","key":"694_CR54","first-page":"229","volume":"99","author":"M Roesch","year":"1999","unstructured":"Roesch, M., et al.: Snort: lightweight intrusion detection for networks. Lisa 99(1), 229\u2013238 (1999)","journal-title":"Lisa"},{"key":"694_CR55","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.cose.2018.11.005","volume":"81","author":"B Selvakumar","year":"2019","unstructured":"Selvakumar, B., Muneeswaran, K.: Firefly algorithm based feature selection for network intrusion detection. Comput. Secur. 81, 148\u2013155 (2019)","journal-title":"Comput. Secur."},{"issue":"6","key":"694_CR56","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/s10207-019-00482-7","volume":"19","author":"K Sethi","year":"2020","unstructured":"Sethi, K., et al.: A context-aware robust intrusion detection system: a reinforcement learning-based approach. Int. J. Inf. Secur. 19(6), 657\u2013678 (2020)","journal-title":"Int. J. Inf. Secur."},{"key":"694_CR57","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Lashkari, A.H., Ghorbani, A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: ICISSP (2018)","DOI":"10.5220\/0006639801080116"},{"key":"694_CR58","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.patcog.2019.02.016","volume":"91","author":"S Sharmin","year":"2019","unstructured":"Sharmin, S., et al.: Simultaneous feature selection and discretization based on mutual information. Pattern Recogn. 91, 162\u2013174 (2019)","journal-title":"Pattern Recogn."},{"key":"694_CR59","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.patcog.2019.02.016","volume":"91","author":"S Sharmin","year":"2019","unstructured":"Sharmin, S., et al.: Simultaneous feature selection and discretization based on mutual information. Pattern Recogn. 91, 162\u2013174 (2019)","journal-title":"Pattern Recogn."},{"issue":"1","key":"694_CR60","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/TETCI.2017.2772792","volume":"2","author":"N Shone","year":"2018","unstructured":"Shone, N., et al.: 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":"694_CR61","doi-asserted-by":"crossref","unstructured":"Song, J., Zhu, Z., Price, C.: Feature grouping for intrusion detection system based on hierarchical clustering. In: International Conference on Availability, Reliability, and Security, pp. 270\u2013280. Springer (2014)","DOI":"10.1007\/978-3-319-10975-6_21"},{"issue":"4","key":"694_CR62","doi-asserted-by":"publisher","first-page":"3492","DOI":"10.1016\/j.eswa.2010.08.137","volume":"38","author":"M-Y Su","year":"2011","unstructured":"Su, M.-Y.: Real-time anomaly detection systems for Denial-of-Service attacks by weighted knearest-neighbor classifiers. Expert Syst. Appl. 38(4), 3492\u20133498 (2011)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"694_CR63","doi-asserted-by":"publisher","first-page":"1913","DOI":"10.1007\/s12652-020-02271-w","volume":"12","author":"AA S\u00fczen","year":"2021","unstructured":"S\u00fczen, A.A.: Developing a multi-level intrusion detection system using hybrid-DBN. J. Ambient Intell. Human. Comput. 12(2), 1913\u20131923 (2021)","journal-title":"J. Ambient Intell. Human. Comput."},{"key":"694_CR64","doi-asserted-by":"publisher","unstructured":"Tang, P., Jiang, R., Zhao, M.: Feature selection and design of intrusion detection system based on k-means and triangle area support vector machine. In: 2010 2nd International Conference on Future Networks, pp. 144\u2013148 (2010). https:\/\/doi.org\/10.1109\/ICFN.2010.42.","DOI":"10.1109\/ICFN.2010.42."},{"key":"694_CR65","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., et\u00a0al.: A detailed analysis of the KDD CUP 99 data set. In: CISDA, pp. 1\u20136. IEEE (2009)","DOI":"10.1109\/CISDA.2009.5356528"},{"issue":"1","key":"694_CR66","first-page":"3221","volume":"15","author":"L Van Der Maaten","year":"2014","unstructured":"Van Der Maaten, L.: Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15(1), 3221\u20133245 (2014)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"694_CR67","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3233\/JCS-1999-7103","volume":"7","author":"G Vigna","year":"1999","unstructured":"Vigna, G., Kemmerer, R.A.: Net- STAT: a network-based intrusion detection system. J. Comput. Secur. 7(1), 37\u201371 (1999)","journal-title":"J. Comput. Secur."},{"issue":"9","key":"694_CR68","doi-asserted-by":"publisher","first-page":"6225","DOI":"10.1016\/j.eswa.2010.02.102","volume":"37","author":"G Wang","year":"2010","unstructured":"Wang, G., et al.: A new approach to intrusion detection using artificial neural networks and fuzzy clustering. Expert Syst. Appl. 37(9), 6225\u20136232 (2010)","journal-title":"Expert Syst. Appl."},{"key":"694_CR69","doi-asserted-by":"publisher","first-page":"41238","DOI":"10.1109\/ACCESS.2018.2858277","volume":"6","author":"B Yan","year":"2018","unstructured":"Yan, B., Han, G.: Effective feature extraction via stacked sparse autoencoder to improve intrusion detection system. IEEE Access 6, 41238\u201341248 (2018)","journal-title":"IEEE Access"},{"key":"694_CR70","doi-asserted-by":"crossref","unstructured":"Yang, X., Tian, Y.: EigenJointsbased action recognition using Na\u00efve-Bayes-Nearest- Neighbor. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 14\u201319 (2012)","DOI":"10.1109\/CVPRW.2012.6239232"},{"key":"694_CR71","doi-asserted-by":"crossref","unstructured":"Yin, C., et al.: A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5, 21954\u201321961 (2017)","DOI":"10.1109\/ACCESS.2017.2762418"},{"key":"694_CR72","doi-asserted-by":"crossref","unstructured":"Zhang, G.: An improvement of payloadbased intrusion detection using fuzzy support vector machine. In: 2nd International Workshop on Intelligent Systems and Applications, vol. 2010, pp. 1\u20134 (2010)","DOI":"10.1109\/IWISA.2010.5473265"},{"key":"694_CR73","doi-asserted-by":"crossref","unstructured":"Zhang, X., Chen, J.: Deep learning based intelligent intrusion detection. In: IEEE 9th International Conference on Communication Software and Networks (ICCSN), vol. 2017, pp. 1133\u20131137. IEEE (2017)","DOI":"10.1109\/ICCSN.2017.8230287"}],"container-title":["International Journal of Information Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-023-00694-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10207-023-00694-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-023-00694-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T02:08:23Z","timestamp":1695694103000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10207-023-00694-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,5]]},"references-count":73,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["694"],"URL":"https:\/\/doi.org\/10.1007\/s10207-023-00694-y","relation":{},"ISSN":["1615-5262","1615-5270"],"issn-type":[{"value":"1615-5262","type":"print"},{"value":"1615-5270","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,5]]},"assertion":[{"value":"5 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with animals performed by any of the authors","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}