{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T06:31:00Z","timestamp":1721284260809},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2014,11,5]],"date-time":"2014-11-05T00:00:00Z","timestamp":1415145600000},"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":["Soft Comput"],"published-print":{"date-parts":[[2015,11]]},"DOI":"10.1007\/s00500-014-1479-2","type":"journal-article","created":{"date-parts":[[2014,11,4]],"date-time":"2014-11-04T16:21:15Z","timestamp":1415118075000},"page":"3237-3248","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Enhancement of spam detection mechanism based on hybrid $$\\varvec{k}$$ k -mean clustering and support vector machine"],"prefix":"10.1007","volume":"19","author":[{"given":"Nadir Omer Fadl","family":"Elssied","sequence":"first","affiliation":[]},{"given":"Othman","family":"Ibrahim","sequence":"additional","affiliation":[]},{"given":"Ahmed Hamza","family":"Osman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2014,11,5]]},"reference":[{"key":"1479_CR1","doi-asserted-by":"crossref","unstructured":"Alguliev RM, Aliguliyev RM, Nazirova SA (2011) Classification of textual e-mail spam using data mining techniques. Appl Comput Intell Soft Comput 2011:1\u20138 Art. ID 416308","DOI":"10.1155\/2011\/416308"},{"key":"1479_CR2","doi-asserted-by":"crossref","unstructured":"Alguliyev R, Nazirova S (2012) Two approaches on implementation of CBR and CRM technologies to the spam filtering problem. Inf J","DOI":"10.4236\/jis.2012.31002"},{"issue":"2","key":"1479_CR3","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.camwa.2011.07.068","volume":"63","author":"A Castiglione","year":"2012","unstructured":"Castiglione A et al (2012) An asynchronous covert channel using spam. Comput Math Appl 63(2):437\u2013447","journal-title":"Comput Math Appl"},{"key":"1479_CR4","doi-asserted-by":"crossref","unstructured":"Chhabra P, Wadhvani R, Shukla S (2010) Spam filtering using support vector machine. In: ACCTA-2010, pp 166\u2013171","DOI":"10.47893\/IJCCT.2010.1053"},{"key":"1479_CR5","unstructured":"DeBarr D, Wechsler H (2009) Spam detection using clustering, random forests, and active learning. In: CEAS 2009, California, USA"},{"issue":"5","key":"1479_CR6","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1109\/72.788645","volume":"10","author":"H Drucker","year":"1999","unstructured":"Drucker H, Wu D, Vapnik VN (1999) Support vector machines for spam categorization. Neural Netw IEEE Trans 10(5):1048\u20131054","journal-title":"Neural Netw IEEE Trans"},{"key":"1479_CR7","doi-asserted-by":"crossref","unstructured":"Golovko V et al (2010) Neural network and artificial immune systems for malware and network intrusion detection. In: Proccedings of advances in machine learning II, pp 485\u2013513","DOI":"10.1007\/978-3-642-05179-1_23"},{"issue":"7","key":"1479_CR8","doi-asserted-by":"crossref","first-page":"10206","DOI":"10.1016\/j.eswa.2009.02.037","volume":"36","author":"TS Guzella","year":"2009","unstructured":"Guzella TS, Caminhas WM (2009) A review of machine learning approaches to spam filtering. Expert Syst Appl 36(7):10206\u201310222","journal-title":"Expert Syst Appl"},{"key":"1479_CR9","doi-asserted-by":"crossref","unstructured":"Hayati P, Potdar V (2008) Evaluation of spam detection and prevention frameworks for email and image spam: a state of art. In: Proceedings of ACM","DOI":"10.1145\/1497308.1497402"},{"key":"1479_CR10","unstructured":"Hopkins M et al (1999) Spambase dataset. https:\/\/archive.ics.usci.edu\/ml\/datasets\/spambase"},{"key":"1479_CR11","unstructured":"Idris I (2011) E-mail spam classification with artificial neural network and negative selection algorithm. Int J Comput Sci 1(3):227\u2013231"},{"key":"1479_CR12","doi-asserted-by":"crossref","unstructured":"Idris I (2012a) Model and algorithm in artificial immune system for spam detection. Int J 3(1):83\u201394","DOI":"10.5121\/ijaia.2012.3107"},{"key":"1479_CR13","unstructured":"Idris I (2012b) Optimized spam classification approach with negative selection algorithm. J Theor Appl Inf Technol 39(1):22\u201331"},{"key":"1479_CR14","doi-asserted-by":"crossref","unstructured":"Jin Q, Ming M (2011) A method to construct self set for IDS based on negative selection algorithm. In: Proceedings of IEEE","DOI":"10.1109\/MEC.2011.6025646"},{"key":"1479_CR15","doi-asserted-by":"crossref","unstructured":"Lai CC, Wu CH (2007) Particle swarm optimization-aided feature selection for spam email classification. In: Proceedings of IEEE","DOI":"10.1109\/ICICIC.2007.442"},{"key":"1479_CR16","doi-asserted-by":"crossref","unstructured":"Lee SM et al (2010) Spam detection using feature selection and parameters optimization. In: Proceedings of IEEE","DOI":"10.1109\/CISIS.2010.116"},{"key":"1479_CR17","unstructured":"Long X, Cleveland WL, Yao YL (2011) Methods and systems for identifying and localizing objects based on features of the objects that are mapped to a vector, Google patents"},{"key":"1479_CR18","unstructured":"MacQueen J (1967) Some methods for classification and analysis of multivariate observations. California, USA"},{"key":"1479_CR19","unstructured":"Marsono MN (2007) Towards improving e-mail content classification for spam control: architecture, abstraction, and strategies. PhD Thesis, University of Victoria"},{"key":"1479_CR20","doi-asserted-by":"crossref","unstructured":"Ma W, Tran D, Sharma D (2009) A novel spam email detection system based on negative selection. In: Proceedings of IEEE","DOI":"10.1109\/ICCIT.2009.58"},{"key":"1479_CR21","unstructured":"Mazid MM, Ali ABMS, Tickle KS (2010) Improved C4.5 algorithm for rule based classification recent advances in artificial intelligence, knowledge engineering and data bases"},{"issue":"4","key":"1479_CR22","doi-asserted-by":"crossref","first-page":"3827","DOI":"10.1016\/j.asoc.2011.02.021","volume":"11","author":"AH Mohammad","year":"2011","unstructured":"Mohammad AH, Zitar RA (2011) Application of genetic optimized artificial immune system and neural networks in spam detection. Appl Soft Comput 11(4):3827\u20133845","journal-title":"Appl Soft Comput"},{"key":"1479_CR23","unstructured":"Morariu DI, Vintan LN, Tresp V (2006) Evolutionary feature selection for text documents using the SVM. Trans Eng Comput Tech 15:215\u2013221"},{"key":"1479_CR24","unstructured":"M\u00fcnz G, Li S, Carle G (2007) Traffic anomaly detection using k-means clustering"},{"key":"1479_CR25","unstructured":"Naksomboon S, Charnsripinyo C, Wattanapongsakorn N (2010) Considering behavior of sender in spam mail detection. In: Proceedings of 6th international conference on networked computing (INC)"},{"issue":"12","key":"1479_CR26","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1038\/nbt1206-1565","volume":"24","author":"WS Noble","year":"2006","unstructured":"Noble WS (2006) What is a support vector machine? Nature Biotechnol 24(12):1565\u20131567","journal-title":"Nature Biotechnol"},{"key":"1479_CR27","unstructured":"Nosrati L, Pour AN (2011) DWM-CDD: dynamic weighted majority concept drift detection for spam mail filtering world academy of science. Eng Technol 80:2011"},{"issue":"1","key":"1479_CR28","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1016\/j.asoc.2012.08.045","volume":"13","author":"F Palmieri","year":"2013","unstructured":"Palmieri F et al (2013) On the detection of card-sharing traffic through wavelet analysis and support vector machines. Appl Soft Comput 13(1):615\u2013627","journal-title":"Appl Soft Comput"},{"key":"1479_CR29","doi-asserted-by":"crossref","unstructured":"Palmieri F, Fiore U, Castiglione A (2014) A distributed approach to network anomaly detection based on independent component analysis. Concurr Comput Pract Exp 26(5):1113\u20131129","DOI":"10.1002\/cpe.3061"},{"issue":"1","key":"1479_CR30","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1093\/biomet\/13.1.25","volume":"13","author":"K Pearson","year":"1920","unstructured":"Pearson K (1920) Notes on the history of correlation. Biometrika 13(1):25\u201345","journal-title":"Biometrika"},{"key":"1479_CR31","unstructured":"Radicati S, Khmartseva M (2009) Email statistics report, 2009\u20132013 May. Radicati Group. www.radicati.com\/wp\/wp-content\/uploads\/2009\/05\/email-stats-report-exec-summary.pdf . Accessed 5 Mar 2010)"},{"key":"1479_CR32","unstructured":"Rao IKR (2003) Data mining and clustering techniques"},{"issue":"6","key":"1479_CR33","first-page":"203","volume":"11","author":"SS Raskar","year":"2011","unstructured":"Raskar SS, Thakore D (2011) Text mining and clustering analysis. IJCSNS 11(6):203","journal-title":"IJCSNS"},{"issue":"2","key":"1479_CR34","first-page":"66","volume":"12","author":"O Saad","year":"2012","unstructured":"Saad O, Darwish A, Faraj R (2012) A survey of machine learning techniques for Spam filtering. IJCSNS 12(2):66","journal-title":"IJCSNS"},{"key":"1479_CR35","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.ins.2012.01.022","volume":"195","author":"F Salcedo-Campos","year":"2012","unstructured":"Salcedo-Campos F, D\u00edaz-Verdejo J, Garc\u00eda-Teodoro P (2012) Segmental parameterisation and statistical modelling of e-mail headers for spam detection. Inf Sci 195:45\u201361","journal-title":"Inf Sci"},{"key":"1479_CR36","doi-asserted-by":"crossref","unstructured":"Salehi S, Selamat A (2011) Hybrid simple artificial immune system (SAIS) and particle swarm optimization (PSO) for spam detection. In: Proceedings of IEEE","DOI":"10.1109\/MySEC.2011.6140655"},{"issue":"2","key":"1479_CR37","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1109\/TNN.2009.2036999","volume":"21","author":"J Sun","year":"2010","unstructured":"Sun J et al (2010) Analysis of the distance between two classes for tuning SVM hyperparameters. Neural Netw IEEE Trans 21(2):305\u2013318","journal-title":"Neural Netw IEEE Trans"},{"key":"1479_CR38","unstructured":"Tafazzoli T, Sadjadi SH (2009) A combined method for detecting spam machines on a target network. Int J Comput Netw Commun (IJCNC) 1(2):35\u201344"},{"issue":"3","key":"1479_CR39","first-page":"17","volume":"3","author":"F Temitayo","year":"2012","unstructured":"Temitayo F, Stephen O, Abimbola A (2012) Hybrid GA-SVM for efficient feature selection in e-mail classification. Comput Eng Intell Syst 3(3):17\u201328","journal-title":"Comput Eng Intell Syst"},{"key":"1479_CR40","unstructured":"Torres GJ, Basnet RB, Sung AH, Mukkamala S, Ribero BM (2009) A similarity measure for clustering and its applications. Int J Electr Comput Syst Eng 3(3):164\u2013170"},{"key":"1479_CR41","unstructured":"Vinther M (2002) Intelligent junk mail detection using neural networks. http:\/\/www.logicnet.dk\/reports\/JunkDetection\/JunkDetection.pdf"},{"key":"1479_CR42","doi-asserted-by":"crossref","unstructured":"Wang L (2005) Support vector machines: theory and applications. vol. 177, pp 1\u201347. Springer, Auckland, New Zealand","DOI":"10.1007\/10984697_1"},{"key":"1479_CR43","unstructured":"Wang X, Cloete I (2005) Learning to classify email: a survey. In: Proceedings of IEEE"},{"issue":"1","key":"1479_CR44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","volume":"14","author":"X Wu","year":"2008","unstructured":"Wu X et al (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14(1):1\u201337","journal-title":"Knowl Inf Syst"},{"issue":"3","key":"1479_CR45","doi-asserted-by":"crossref","first-page":"4321","DOI":"10.1016\/j.eswa.2008.03.002","volume":"36","author":"CH Wu","year":"2009","unstructured":"Wu CH (2009) Behavior-based spam detection using a hybrid method of rule-based techniques and neural networks. Expert Syst Appl 36(3):4321\u20134330","journal-title":"Expert Syst Appl"},{"key":"1479_CR46","doi-asserted-by":"crossref","unstructured":"Xie Y et al (2008) Spamming botnets: signatures and characteristics. In: Proceedings of ACM","DOI":"10.1145\/1402958.1402979"},{"key":"1479_CR47","doi-asserted-by":"crossref","unstructured":"Youn S, McLeod D (2007) A comparative study for email classification. Computing Sciences and Software Engineering, Advances and Innovations in Systems, pp 387\u2013391","DOI":"10.1007\/978-1-4020-6264-3_67"},{"issue":"4","key":"1479_CR48","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.knosys.2008.01.001","volume":"21","author":"B Yu","year":"2008","unstructured":"Yu B, Xu Z (2008) A comparative study for content-based dynamic spam classification using four machine learning algorithms. Knowl Based Syst 21(4):355\u2013362","journal-title":"Knowl Based Syst"},{"key":"1479_CR49","doi-asserted-by":"crossref","unstructured":"Zhang Q et al (2011) Fuzzy clustering based on semantic body and its application in Chinese spam filtering. JDCTA: Int J Digital Content Technol Appl 5(4):1\u201311","DOI":"10.4156\/jdcta.vol5.issue4.1"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-014-1479-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-014-1479-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-014-1479-2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T05:22:31Z","timestamp":1650518551000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-014-1479-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,11,5]]},"references-count":49,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2015,11]]}},"alternative-id":["1479"],"URL":"https:\/\/doi.org\/10.1007\/s00500-014-1479-2","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,11,5]]}}}
