{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T01:54:30Z","timestamp":1781574870717,"version":"3.54.5"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031083327","type":"print"},{"value":"9783031083334","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-08333-4_9","type":"book-chapter","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T11:52:13Z","timestamp":1655380333000},"page":"108-115","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced Dependency-Based Feature Selection to\u00a0Improve Anomaly Network Intrusion Detection"],"prefix":"10.1007","author":[{"given":"K.","family":"Bennaceur","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Z.","family":"Sahraoui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. A.","family":"Nacer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,10]]},"reference":[{"issue":"30","key":"9_CR1","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1016\/j.cose.2011.08.009","volume":"30","author":"C Kolias","year":"2011","unstructured":"Kolias, C., Kambourakis, G., Maragoudakis, M.: Swarm intelligence in intrusion detection: a survey. J. Comput. Secur. 30(30), 625\u2013642 (2011)","journal-title":"J. Comput. Secur."},{"issue":"5","key":"9_CR2","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1002\/cpe.3061","volume":"26","author":"F Palmieri","year":"2014","unstructured":"Palmieri, F., Fiore, U., Castiglione, A.: A distributed approach to network anomaly detection based on independent component analysis. Concurr. Comput. Pract. Exp. 26(5), 1113\u20131129 (2014)","journal-title":"Concurr. Comput. Pract. Exp."},{"issue":"4","key":"9_CR3","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1109\/TSMCC.2007.897498","volume":"37","author":"M Banerjee","year":"2007","unstructured":"Banerjee, M., Mitra, S., Banka, H.: Evolutionary rough feature selection in gene expression data. IEEE Trans. Syst. Man Cybern. 37(4), 622\u2013632 (2007)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"9_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1007\/978-3-319-46487-9_53","volume-title":"Computer Vision \u2013 ECCV 2016","author":"MM Cheng","year":"2016","unstructured":"Cheng, M.M., et al.: HFS: hierarchical feature selection for\u00a0efficient image segmentation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 867\u2013882. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_53"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Middlemiss, M.J., Dick, G.: Weighted feature extraction using a genetic algorithm for intrusion detection. In: Congress on Evolutionary Computation, vol. 3, pp. 1669\u20131675 (2003)","DOI":"10.1109\/CEC.2003.1299873"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Aminanto, M.E., Choi, R., Tanuwidjaja, H.C., Yoo, P.D., Kim, K.: Deep abstraction and weighted feature selection for Wi-Fi impersonation detection. In: IEEE Trans. Inf. Forens. Secur. 13(3), 621\u2013636 (2017)","DOI":"10.1109\/TIFS.2017.2762828"},{"issue":"1","key":"9_CR7","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I Guyon","year":"2002","unstructured":"Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1), 389\u2013422 (2002)","journal-title":"Mach. Learn."},{"key":"9_CR8","unstructured":"Ratanamahatana, C.A., Gunopulos, D.: Scaling up the Naive Bayesian classifier: using decision trees for feature selection (2002)"},{"issue":"5","key":"9_CR9","doi-asserted-by":"publisher","first-page":"2428","DOI":"10.1109\/TIP.2018.2886761","volume":"28","author":"F Nie","year":"2018","unstructured":"Nie, F., Yang, S., Zhang, R., Li, X.: A general framework for auto-weighted feature selection via global redundancy minimization. IEEE Trans. Image Process. 28(5), 2428\u20132438 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Balasaraswathi, V.R., Sugumaran, M., Hamid, Y.: Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms. J. Commun. Inf. Netw. 2(4), 107\u2013119 (2017)","DOI":"10.1007\/s41650-017-0033-7"},{"issue":"1","key":"9_CR11","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. Elect. Eng. 40(1), 16\u201328 (2014)","journal-title":"Comput. Elect. Eng."},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: 4th International Conference on Information Systems Security and Privacy (2018)","DOI":"10.5220\/0006639801080116"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: IEEE Symposium on Computational Intelligence For Security and Defense Applications, pp. 1\u20136 (2009)","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Ferrag, M.A., Maglaras, L., Moschoyiannis, S., Janicke, H.: Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study. J. Inf. Secur. App. 50, 102419 (2020)","DOI":"10.1016\/j.jisa.2019.102419"},{"key":"9_CR15","unstructured":"Hindy, H., et al.: A taxonomy and survey of intrusion detection system design techniques, network threats and datasets. arXiv preprint (2018)"},{"key":"9_CR16","unstructured":"Breiman, L., Friedman, J., Stone, C., Olshen, J., Richard, A.: Classification and Regression Trees. CRC Press, Boca Raton (1984)"}],"container-title":["IFIP Advances in Information and Communication Technology","Artificial Intelligence Applications and Innovations"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-08333-4_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T01:40:05Z","timestamp":1781574005000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-08333-4_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031083327","9783031083334"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-08333-4_9","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"value":"1868-4238","type":"print"},{"value":"1868-422X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"10 June 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Artificial Intelligence Applications and Innovations","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hersonissos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ifipaiai.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}