{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:54:54Z","timestamp":1781020494996,"version":"3.54.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2017,7,4]],"date-time":"2017-07-04T00:00:00Z","timestamp":1499126400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2019,3]]},"DOI":"10.1007\/s10462-017-9567-1","type":"journal-article","created":{"date-parts":[[2017,7,4]],"date-time":"2017-07-04T11:56:24Z","timestamp":1499169384000},"page":"403-443","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":104,"title":["A hybrid intrusion detection system (HIDS) based on prioritized k-nearest neighbors and optimized SVM classifiers"],"prefix":"10.1007","volume":"51","author":[{"given":"Ahmed I.","family":"Saleh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatma M.","family":"Talaat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Labib M.","family":"Labib","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2017,7,4]]},"reference":[{"key":"9567_CR1","unstructured":"Aksoy S (2008) Feature reduction and selection. Department of Computer Engineering, Bilkent University, CS 551"},{"issue":"8","key":"9567_CR2","first-page":"2663","volume":"21","author":"SO Al-mamory","year":"2013","unstructured":"Al-mamory SO, Jassim FS (2013) Evaluation of different data mining algorithms with KDD CUP 99 data set. J Babylon Univ Pure Appl Sci 21(8):2663\u20132681","journal-title":"J Babylon Univ Pure Appl Sci"},{"issue":"6","key":"9567_CR3","first-page":"225","volume":"2","author":"MA Amrita","year":"2013","unstructured":"Amrita MA (2013) Performance analysis of different feature selection methods in intrusion detection. Int J Sci Technol Res 2(6):225\u2013231","journal-title":"Int J Sci Technol Res"},{"key":"9567_CR4","unstructured":"Atefi K, Yahya S, Dak AY, Atefi A (2013) A hybrid intrusion detection system based on different machine learning algorithms. In: Proceedings of the 4th international conference on computing and informatics, Sarawak, Malaysia. pp 312\u2013320"},{"key":"9567_CR5","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1049\/el:20020467","volume":"38","author":"Y Bin","year":"2002","unstructured":"Bin Y, Qiao Y, Xin XW et al (2002) Anomaly intrusion detection method based on HMM[J]. IEEE Electron Lett 38:663\u2013664","journal-title":"IEEE Electron Lett"},{"key":"9567_CR6","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.cose.2014.06.006","volume":"45","author":"R Chitrakar","year":"2014","unstructured":"Chitrakar R, Huang C (2014) Selection of candidate support vectors in incremental SVM for network intrusion detection. Comput Secur 45:231\u2013241","journal-title":"Comput Secur"},{"issue":"12","key":"9567_CR7","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1109\/LSP.2002.806070","volume":"9","author":"M Davy","year":"2002","unstructured":"Davy M, Gretton A, Doucet A, Rayner PJW (2002) Optimized support vector machines for nonstationary signal classification. IEEE Signal Process Lett 9(12):442\u2013445","journal-title":"IEEE Signal Process Lett"},{"key":"9567_CR8","unstructured":"Devarakondaa N, Pamidib S, Kumari VV, Govardhan A (2011) Intrusion detection system using bayesian network and hidden Markov model. In: Selection and\/or peer-review under responsibility of C3IT. Elsevier Ltd"},{"key":"9567_CR9","doi-asserted-by":"crossref","unstructured":"Di Martino S, Ferrucci F, Gravino C, Sarro F (2011) A genetic algorithm to configure support vector machines for predicting fault-prone components. In: Product-focused software process improvement. Springer, pp 247\u2013261","DOI":"10.1007\/978-3-642-21843-9_20"},{"key":"9567_CR10","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.future.2013.06.027","volume":"37","author":"W Feng","year":"2014","unstructured":"Feng W, Zhang Q, Hu G, Huang JX (2014) Mining network data for intrusion detection through combining SVMs with ant colony networks. Future Gener Comput Syst 37:127\u2013140","journal-title":"Future Gener Comput Syst"},{"key":"9567_CR11","doi-asserted-by":"crossref","unstructured":"Frohlich H, Chapelle O (2003) Feature selection for support vector machines by means of genetic algorithm. In: Proceedings of the 15th IEEE international conference on tools with artificial intelligence, Sacramento, 3\u20135 November. pp 142\u2013148","DOI":"10.1109\/TAI.2003.1250182"},{"key":"9567_CR12","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1109\/JSEN.2002.800688","volume":"2","author":"R Gutierrez-Osuna","year":"2002","unstructured":"Gutierrez-Osuna R (2002) Pattern analysis for machine olfaction: a review. IEEE Sens J 2:189\u2013202","journal-title":"IEEE Sens J"},{"key":"9567_CR13","unstructured":"Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification, Technical report. Department of Computer Science and Information Engineering, University of National Taiwan, Taipei. pp 1\u201312"},{"key":"9567_CR14","unstructured":"Kayacik HG, Zincir-Heywood AN, Heywood MI (2005) 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 , October 12\u201314, 2005, The Fairmont Algonquin, St. Andrews, New Brunswick, Canada"},{"key":"9567_CR15","unstructured":"KDD Cup (1999) Intrusion detection dataset. \n                    http:\/\/kdd.ics.uci.edu\/databases\/kddcup99\/kddcup99.html"},{"key":"9567_CR16","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol IV. pp 1942\u20131948","DOI":"10.1109\/ICNN.1995.488968"},{"key":"9567_CR17","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.asoc.2014.01.028","volume":"18","author":"F Kuang","year":"2014","unstructured":"Kuang F, Xu W, Zhang S (2014) A novel hybrid KPCA and SVM with GA model for intrusion detection. Appl Soft Comput 18:178\u2013184","journal-title":"Appl Soft Comput"},{"key":"9567_CR18","doi-asserted-by":"crossref","unstructured":"Kuang F, Zhang S, Jin Z, Xu W (2015) A novel SVM by combining kernel principal component analysis and improved chaotic particle swarm optimization for intrusion detection. Soft Comput 21:1\u201313","DOI":"10.1007\/s00500-014-1332-7"},{"key":"9567_CR19","doi-asserted-by":"crossref","unstructured":"Le Thi HA, Le AV, Vo XT, Zidna A (2014) A filter based feature selection approach in MSVM using DCA and its application in network intrusion detection. In: Nguyen NT, Attachoo B, Trawi\u0144ski B, Somboonviwat K (eds) Intelligent information and database systems. ACIIDS 2014. Lecture notes in computer science, vol 8398. Springer, Cham","DOI":"10.1007\/978-3-319-05458-2_42"},{"key":"9567_CR20","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1109\/TKDE.2005.135","volume":"17","author":"H Liu","year":"2005","unstructured":"Liu H, Yu L (2005) Towards integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17:491\u2013502","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9567_CR21","doi-asserted-by":"crossref","unstructured":"Mukkamala S, Janoski G, Sung AH (2002) Intrusion detection using neural networks and support vector machines. In: Proceedings of IEEE international joint conference on neural networks, vol 2. Honolulu, pp 1702\u20131707","DOI":"10.1109\/IJCNN.2002.1007774"},{"key":"9567_CR22","unstructured":"Olusola AA, Oladele AS, Abosede DO (2010) Analysis of KDD\u201999 intrusion detection dataset for selection of relevance features. In: Proceedings of the world congress on engineering and computer science, vol 1"},{"key":"9567_CR23","doi-asserted-by":"crossref","unstructured":"Roobaert D, Karakoulas G, Chawla NV (2006) Information gain, correlation and support vector machines. In: Feature extraction. Springer, Berlin, pp 463\u2013470","DOI":"10.1007\/978-3-540-35488-8_23"},{"key":"9567_CR24","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.knosys.2014.12.002","volume":"75","author":"AI Saleh","year":"2015","unstructured":"Saleh AI, El Desouky AI, Ali SH (2015) Promoting the performance of vertical recommendation systems by applying new classification techniques. Knowl Based Syst 75:192\u2013223","journal-title":"Knowl Based Syst"},{"key":"9567_CR25","doi-asserted-by":"publisher","unstructured":"Song J, Takakura H, Okabe Y, Eto M, Inoue D, Nakao K (2011) Statistical analysis of honeypot data and building of Kyoto 2006+ dataset for NIDS evaluation. In: Proceedings of the 1st workshop on building analysis datasets and gathering experience returns for security, Salzburg, 10\u201313 April 2011. pp 29\u201336. doi:\n                    10.1145\/1978672.1978676","DOI":"10.1145\/1978672.1978676"},{"key":"9567_CR26","doi-asserted-by":"crossref","unstructured":"Sravani K, Srinivasu P (2014) Comparative study of machine learning algorithm for intrusion detection system. In: Satapathy S, Udgata S, Biswal B (eds) Proceedings of the international conference on frontiers of intelligent computing: theory and applications (FICTA) 2013. Advances in intelligent systems and computing, vol 247. Springer, Cham","DOI":"10.1007\/978-3-319-02931-3_23"},{"key":"9567_CR27","unstructured":"Subaira AS, Anitha P (2013) An efficient classification mechanism for network intrusion detection system based on data mining techniques: a survey. ISSN: 1694-2108"},{"issue":"3","key":"9567_CR28","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1109\/MCC.2014.53","volume":"3","author":"Z Tan","year":"2014","unstructured":"Tan Z, Nagar UT, He X, Liu RP, Wang S, Hu J (2014) Enhancing big data security with collaborative intrusion detection. IEEE Cloud Comput 3(3):27\u201333","journal-title":"IEEE Cloud Comput"},{"key":"9567_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3264-1","volume-title":"The nature of statistical learning theory","author":"VN Vapnik","year":"2000","unstructured":"Vapnik VN (2000) The nature of statistical learning theory. Springer, New York"},{"key":"9567_CR30","first-page":"227","volume":"9","author":"GP Wang","year":"2015","unstructured":"Wang GP, Chen SY, Liu J (2015) Anomaly-based intrusion detection using multiclass-SVM with parameters optimized by PSO. Int J Secur Appl 9:227\u2013242","journal-title":"Int J Secur Appl"},{"key":"9567_CR31","doi-asserted-by":"crossref","unstructured":"Warrender C, Forrest S, Pearlmutter B (1999) Detecting intrusion using system calls: alternative data models. In: IEEE symposium on security and privacy. IEEE Computer Society","DOI":"10.1109\/SECPRI.1999.766910"},{"key":"9567_CR32","doi-asserted-by":"publisher","first-page":"7698","DOI":"10.1016\/j.eswa.2010.12.141","volume":"38","author":"Y Yi","year":"2011","unstructured":"Yi Y, Wu J, Xu W (2011) Incremental SVM based on reserved set for network intrusion detection. Expert Syst Appl 38:7698\u20137707","journal-title":"Expert Syst Appl"},{"key":"9567_CR33","unstructured":"Yu L, Liu H (2003) Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Machine learning-international workshop then conference, vol 20. p 856"},{"key":"9567_CR34","doi-asserted-by":"crossref","unstructured":"Zhang M, Yao JT (2004) A rough sets based approach to feature selection. In: IEEE annual meeting of the fuzzy information, processing NAFIPS\u201904, vol 1. IEEE, pp 434\u2013439","DOI":"10.1109\/NAFIPS.2004.1336322"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-017-9567-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10462-017-9567-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-017-9567-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,1]],"date-time":"2019-04-01T07:28:24Z","timestamp":1554103704000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10462-017-9567-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7,4]]},"references-count":34,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,3]]}},"alternative-id":["9567"],"URL":"https:\/\/doi.org\/10.1007\/s10462-017-9567-1","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,7,4]]},"assertion":[{"value":"4 July 2017","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}