{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:34:53Z","timestamp":1782844493278,"version":"3.54.5"},"reference-count":102,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T00:00:00Z","timestamp":1565049600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T00:00:00Z","timestamp":1565049600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1007\/s00521-019-04396-2","type":"journal-article","created":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T19:03:17Z","timestamp":1565118197000},"page":"3475-3501","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["NSNAD: negative selection-based network anomaly detection approach with relevant feature subset"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3250-0803","authenticated-orcid":false,"given":"Naila","family":"Belhadj aissa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Guerroumi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdelouahid","family":"Derhab","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,8,6]]},"reference":[{"key":"4396_CR1","doi-asserted-by":"publisher","unstructured":"Abas EAER, Abdelkader H, Keshk A (2015) Artificial immune system based intrusion detection. In: 2015 IEEE seventh international conference on intelligent computing and information systems (ICICIS), pp 542\u2013546. Institute of Electrical & Electronics Engineers (IEEE). https:\/\/doi.org\/10.1109\/intelcis.2015.7397274","DOI":"10.1109\/intelcis.2015.7397274"},{"issue":"8","key":"4396_CR2","doi-asserted-by":"publisher","first-page":"335","DOI":"10.14257\/ijsia.2016.10.8.29","volume":"10","author":"A Agrawal","year":"2016","unstructured":"Agrawal A, Mohammed S, Fiaidhi J (2016) Developing data mining techniques for intruder detection in network traffic. Int J Secur Appl 10(8):335\u2013342. https:\/\/doi.org\/10.14257\/ijsia.2016.10.8.29","journal-title":"Int J Secur Appl"},{"key":"4396_CR3","unstructured":"Al-Enezi J, Abbod M, Alsharhan S (2010) Artificial immune systems-models, algorithms and applications. http:\/\/www.arpapress.com\/Volumes\/Vol3Issue2\/IJRRAS_3_2_01.pdf"},{"issue":"10","key":"4396_CR4","doi-asserted-by":"publisher","first-page":"2986","DOI":"10.1109\/TC.2016.2519914","volume":"65","author":"MA Ambusaidi","year":"2016","unstructured":"Ambusaidi MA, He X, Nanda P, Tan Z (2016) Building an intrusion detection system using a filter-based feature selection algorithm. IEEE Trans Comput 65(10):2986\u20132998. https:\/\/doi.org\/10.1109\/TC.2016.2519914","journal-title":"IEEE Trans Comput"},{"issue":"2","key":"4396_CR5","first-page":"23","volume":"13","author":"SH Amer","year":"2010","unstructured":"Amer SH, Hamilton J (2010) Intrusion detection systems (ids) taxonomy-a short review. Def Cyber Secur 13(2):23\u201330","journal-title":"Def Cyber Secur"},{"issue":"02","key":"4396_CR6","doi-asserted-by":"publisher","first-page":"11","DOI":"10.4236\/jdaip.2015.32002","volume":"3","author":"A Ammar","year":"2015","unstructured":"Ammar A (2015) Comparison of feature reduction techniques for the binominal classification of network traffic. J Data Anal Inf Process 3(02):11. https:\/\/doi.org\/10.4236\/jdaip.2015.32002","journal-title":"J Data Anal Inf Process"},{"issue":"4","key":"4396_CR7","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1080\/19361610.2016.1211847","volume":"11","author":"K Anusha","year":"2016","unstructured":"Anusha K, Sathiyamoorthy E (2016) Omamids: ontology based multi-agent model intrusion detection system for detecting web service attacks. J Appl Secur Res 11(4):489\u2013508. https:\/\/doi.org\/10.1080\/19361610.2016.1211847","journal-title":"J Appl Secur Res"},{"key":"4396_CR8","unstructured":"Axelsson S (2000) Intrusion detection systems: a survey and taxonomy. Report, Technical report"},{"key":"4396_CR9","doi-asserted-by":"publisher","unstructured":"Bahl S, Sharma SK (2016) A minimal subset of features using correlation feature selection model for intrusion detection system. In: Proceedings of the second international conference on computer and communication technologies, pp 337\u2013346. Springer. https:\/\/doi.org\/10.1007\/978-81-322-2523-2_32","DOI":"10.1007\/978-81-322-2523-2_32"},{"key":"4396_CR10","doi-asserted-by":"publisher","unstructured":"Bethi SK, Phoha VV, Reddy YM (2004) Clique clustering approach to detect denial-of-service attacks. In: Proceedings from the fifth annual IEEE SMC information assurance workshop 2004, pp 447\u2013448. https:\/\/doi.org\/10.1109\/iaw.2004.1437856","DOI":"10.1109\/iaw.2004.1437856"},{"issue":"1","key":"4396_CR11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/SURV.2013.052213.00046","volume":"16","author":"M Bhuyan","year":"2014","unstructured":"Bhuyan M, Bhattacharyya D, Kalita J (2014) Network anomaly detection: methods, systems and tools. Commun Surv Tutor IEEE 16(1):1\u201334","journal-title":"Commun Surv Tutor IEEE"},{"key":"4396_CR12","unstructured":"Brownlee J (2011) Clever algorithms: nature-inspired programming recipes. Jason Brownlee"},{"key":"4396_CR13","unstructured":"Buitinck L, Louppe G, Blondel M, Pedregosa F, Mueller A, Grisel O, Niculae V, Prettenhofer P, Gramfort A, Grobler J, Layton R, VanderPlas J, Joly A, Holt B, Varoquaux G (2013) API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD workshop: languages for data mining and machine learning, pp 108\u2013122"},{"issue":"2","key":"4396_CR14","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1023\/a:1009715923555","volume":"2","author":"CJ Burges","year":"1998","unstructured":"Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121\u2013167. https:\/\/doi.org\/10.1023\/a:1009715923555","journal-title":"Data Min Knowl Disc"},{"issue":"3","key":"4396_CR15","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1109\/tevc.2002.1011539","volume":"6","author":"L de Castro","year":"2002","unstructured":"de Castro L, Zuben FV (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evol Comput 6(3):239\u2013251. https:\/\/doi.org\/10.1109\/tevc.2002.1011539","journal-title":"IEEE Trans Evol Comput"},{"issue":"8","key":"4396_CR16","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1007\/s00500-002-0237-z","volume":"7","author":"LN de Castro","year":"2003","unstructured":"de Castro LN, Timmis JI (2003) Artificial immune systems as a novel soft computing paradigm. Soft Comput 7(8):526\u2013544","journal-title":"Soft Comput"},{"key":"4396_CR17","unstructured":"Cemerlic A, Yang L, Kizza JM (2008) Network intrusion detection based on bayesian networks. In: SEKE, pp 791\u2013794"},{"key":"4396_CR18","unstructured":"Chan FT, Prakash A, Tibrewal R, Tiwari M (2013) Clonal selection approach for network intrusion detection. In: Proceedings of the 3rd international conference on intelligent computational systems (ICICS\u20192013), Singapore, pp 1\u20135"},{"key":"4396_CR19","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.engappai.2016.01.020","volume":"51","author":"MH Chen","year":"2016","unstructured":"Chen MH, Chang PC, Wu JL (2016) A population-based incremental learning approach with artificial immune system for network intrusion detection. Eng Appl Artif Intell 51:171\u2013181. https:\/\/doi.org\/10.1016\/j.engappai.2016.01.020","journal-title":"Eng Appl Artif Intell"},{"issue":"3","key":"4396_CR20","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/bf00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297. https:\/\/doi.org\/10.1007\/bf00994018","journal-title":"Mach Learn"},{"key":"4396_CR21","unstructured":"Crosbie M, Spafford G (1995) Applying genetic programming to intrusion detection. In: Working notes for the AAAI symposium on genetic programming, pp 1\u20138. MIT Press, Cambridge"},{"key":"4396_CR22","doi-asserted-by":"crossref","unstructured":"DasGupta D (1993) An overview of artificial immune systems and their applications. In: Artificial immune systems and their applications, pp 3\u201321. Springer","DOI":"10.1007\/978-3-642-59901-9_1"},{"key":"4396_CR23","doi-asserted-by":"crossref","DOI":"10.1201\/9781420065466","volume-title":"Immunological computation: theory and applications","author":"D Dasgupta","year":"2008","unstructured":"Dasgupta D, Nino F (2008) Immunological computation: theory and applications. CRC Press, Boca Raton"},{"issue":"2","key":"4396_CR24","doi-asserted-by":"publisher","first-page":"1574","DOI":"10.1016\/j.asoc.2010.08.024","volume":"11","author":"D Dasgupta","year":"2011","unstructured":"Dasgupta D, Yu S, Nino F (2011) Recent advances in artificial immune systems: models and applications. Appl Soft Comput 11(2):1574\u20131587. https:\/\/doi.org\/10.1016\/j.asoc.2010.08.024","journal-title":"Appl Soft Comput"},{"issue":"6","key":"4396_CR25","first-page":"446","volume":"4","author":"L Dhanabal","year":"2015","unstructured":"Dhanabal L, Shantharajah S (2015) A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. Int J Adv Res Comput Commun Eng 4(6):446\u2013452","journal-title":"Int J Adv Res Comput Commun Eng"},{"key":"4396_CR26","doi-asserted-by":"publisher","unstructured":"Ding K, Li J, Liu H (2019) Interactive anomaly detection on attributed networks. In: In the twelfth ACM international conference on web search and data mining (WSDM \u201919). https:\/\/doi.org\/10.1145\/3289600.3290964","DOI":"10.1145\/3289600.3290964"},{"key":"4396_CR27","unstructured":"Empirical rule: What is it? (2017). http:\/\/www.statisticshowto.com\/empirical-rule-2\/"},{"key":"4396_CR28","doi-asserted-by":"publisher","unstructured":"Forrest S, Perelson A, Allen L, Cherukuri R (1994) Self-nonself discrimination in a computer. In: Proceedings of 1994 IEEE computer society symposium on research in security and privacy, p 202. Institute of Electrical & Electronics Engineers (IEEE). https:\/\/doi.org\/10.1109\/risp.1994.296580","DOI":"10.1109\/risp.1994.296580"},{"key":"4396_CR29","unstructured":"Gentile C, Li S, Kar P, Karatzoglou A, Zappella G, Etrue E (2017) On context-dependent clustering of bandits. In: Precup D, Teh YW (eds) Proceedings of the 34th international conference on machine learning, proceedings of machine learning research, vol\u00a070, pp 1253\u20131262. PMLR, International Convention Centre, Sydney, Australia. http:\/\/proceedings.mlr.press\/v70\/gentile17a.html"},{"issue":"4","key":"4396_CR30","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1016\/j.jare.2014.02.009","volume":"6","author":"TF Ghanem","year":"2015","unstructured":"Ghanem TF, Elkilani WS, Abdul-kader HM (2015) A hybrid approach for efficient anomaly detection using metaheuristic methods. J Adv Res 6(4):609\u2013619. https:\/\/doi.org\/10.1016\/j.jare.2014.02.009","journal-title":"J Adv Res"},{"key":"4396_CR31","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-Pino J, Edmonds J, Papa M (2006) Attribute selection using information gain for a fuzzy logic intrusion detection system. In: Defense and security symposium, pp 62410D\u201362410D. International society for optics and photonics","DOI":"10.1117\/12.666611"},{"issue":"4","key":"4396_CR32","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1023\/A:1026195112518","volume":"4","author":"FA Gonz\u00e1lez","year":"2003","unstructured":"Gonz\u00e1lez FA, Dasgupta D (2003) Anomaly detection using real-valued negative selection. Genet Program Evolvable Mach 4(4):383\u2013403","journal-title":"Genet Program Evolvable Mach"},{"key":"4396_CR33","doi-asserted-by":"crossref","unstructured":"Guha S, Yau SS, Buduru AB (2016) Attack detection in cloud infrastructures using artificial neural network with genetic feature selection. In: Dependable, autonomic and secure computing, 14th International conference on pervasive intelligence and computing, 2nd International conf on big data intelligence and computing and cyber science and technology congress (DASC\/PiCom\/DataCom\/CyberSciTech), 2016 IEEE 14th Intl C, pp 414\u2013419. IEEE","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2016.32"},{"issue":"1","key":"4396_CR34","doi-asserted-by":"publisher","first-page":"22","DOI":"10.4304\/jcp.9.1.22-27","volume":"9","author":"H Guo","year":"2014","unstructured":"Guo H, Feng Y, Hao F, Zhong S, Li S (2014) Dynamic fuzzy logic control of genetic algorithm probabilities. J Comput 9(1):22\u201327. https:\/\/doi.org\/10.4304\/jcp.9.1.22-27","journal-title":"J Comput"},{"key":"4396_CR35","unstructured":"Gutierrez MP, Kiekintveld C (2016) Bandits for cybersecurity: adaptive intrusion detection using honeypots. In: AAAI Workshop: Artificial Intelligence for Cyber Security"},{"issue":"1","key":"4396_CR36","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1145\/1656274.1656278","volume":"11","author":"M Hall","year":"2009","unstructured":"Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software. SIGKDD Explor Newsl 11(1):10. https:\/\/doi.org\/10.1145\/1656274.1656278","journal-title":"SIGKDD Explor Newsl"},{"issue":"3","key":"4396_CR37","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1109\/tsc.2015.2401833","volume":"8","author":"F Hao","year":"2015","unstructured":"Hao F, Li S, Min G, Kim HC, Yau SS, Yang LT (2015) An efficient approach to generating location-sensitive recommendations in ad-hoc social network environments. IEEE Trans Serv Comput 8(3):520\u2013533. https:\/\/doi.org\/10.1109\/tsc.2015.2401833","journal-title":"IEEE Trans Serv Comput"},{"issue":"6","key":"4396_CR38","doi-asserted-by":"crossref","first-page":"553","DOI":"10.3390\/su8060553","volume":"8","author":"F Hao","year":"2016","unstructured":"Hao F, Park DS, Li S, Lee HM (2016) Mining $$\\lambda$$-maximal cliques from a fuzzy graph. Sustainability 8(6):553","journal-title":"Sustainability"},{"key":"4396_CR39","doi-asserted-by":"publisher","unstructured":"Hofmann A, Horeis T, Sick B (2004) Feature selection for intrusion detection: an evolutionary wrapper approach. In: 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541), vol\u00a02, pp 1563\u20131568. Institute of Electrical & Electronics Engineers (IEEE). https:\/\/doi.org\/10.1109\/ijcnn.2004.1380189","DOI":"10.1109\/ijcnn.2004.1380189"},{"issue":"4","key":"4396_CR40","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1162\/106365600568257","volume":"8","author":"SA Hofmeyr","year":"2000","unstructured":"Hofmeyr SA, Forrest S (2000) Architecture for an artificial immune system. Evol Comput 8(4):443\u2013473. https:\/\/doi.org\/10.1162\/106365600568257","journal-title":"Evol Comput"},{"key":"4396_CR41","doi-asserted-by":"publisher","unstructured":"Hong L (2008) Artificial immune system for anomaly detection. In: 2008 IEEE international symposium on knowledge acquisition and modeling workshop, pp 340\u2013343. Institute of Electrical & Electronics Engineers (IEEE). https:\/\/doi.org\/10.1109\/kamw.2008.4810493","DOI":"10.1109\/kamw.2008.4810493"},{"key":"4396_CR42","unstructured":"Hoque MS, Mukit M, Bikas M, Naser A, et\u00a0al. (2012) An implementation of intrusion detection system using genetic algorithm. arXiv preprint arXiv:1204.1336"},{"key":"4396_CR43","doi-asserted-by":"crossref","unstructured":"Igbe O, Darwish I, Saadawi T (2016) Distributed network intrusion detection systems: an artificial immune system approach. In: Connected health: applications, systems and engineering technologies (CHASE), 2016 IEEE First International Conference on, pp 101\u2013106. IEEE","DOI":"10.1109\/CHASE.2016.36"},{"key":"4396_CR44","doi-asserted-by":"crossref","unstructured":"Janarthanan T, Zargari S (2017) Feature selection in unsw-nb15 and kddcup\u201999 datasets. In: 2017 IEEE 26th international symposium on industrial electronics (ISIE), pp 1881\u20131886. IEEE","DOI":"10.1109\/ISIE.2017.8001537"},{"key":"4396_CR45","doi-asserted-by":"crossref","unstructured":"Kar P, Li S, Narasimhan H, Chawla S, Sebastiani F (2016) Online optimization methods for the quantification problem. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1625\u20131634. ACM","DOI":"10.1145\/2939672.2939832"},{"issue":"2","key":"4396_CR46","first-page":"271","volume":"2","author":"AG Karegowda","year":"2010","unstructured":"Karegowda AG, Manjunath A, Jayaram M (2010) Comparative study of attribute selection using gain ratio and correlation based feature selection. Int J Inf Technol Knowl Manag 2(2):271\u2013277","journal-title":"Int J Inf Technol Knowl Manag"},{"key":"4396_CR47","doi-asserted-by":"crossref","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","DOI":"10.4018\/978-1-59140-561-0.ch071"},{"key":"4396_CR48","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.cose.2017.06.005","volume":"70","author":"C Khammassi","year":"2017","unstructured":"Khammassi C, Krichen S (2017) A GA-LR wrapper approach for feature selection in network intrusion detection. Comput Secur 70:255\u2013277","journal-title":"Comput Secur"},{"key":"4396_CR49","unstructured":"Kim J, Bentley PJ (2001) Towards an artificial immune system for network intrusion detection: An investigation of clonal selection with a negative selection operator. In: Proceedings of the 2001 congress on evolutionary computation, 2001. vol 2, pp 1244\u20131252. IEEE"},{"key":"4396_CR50","unstructured":"Kim J, Bentley PJ (2002) Towards an artificial immune system for network intrusion detection: an investigation of dynamic clonal selection. In: Proceedings of the 2002 congress on evolutionary computation, 2002. CEC\u201902., vol\u00a02, pp 1015\u20131020. IEEE"},{"key":"4396_CR51","doi-asserted-by":"crossref","unstructured":"Kira K, Rendell LA (1992) A practical approach to feature selection. In: Proceedings of the ninth international workshop on Machine learning, pp 249\u2013256","DOI":"10.1016\/B978-1-55860-247-2.50037-1"},{"key":"4396_CR52","unstructured":"Korda N, Sz\u00f6r\u00e9nyi B, Shuai L (2016) Distributed clustering of linear bandits in peer to peer networks. In: Journal of machine learning research workshop and conference proceedings, vol\u00a048, pp 1301\u20131309. International Machine Learning Society"},{"key":"4396_CR53","unstructured":"Kumar V, Chauhan H, Panwar D (2013) K-means clustering approach to analyze NSL-KDD intrusion detection dataset. International Journal of Soft Computing and Engineering (IJSCE) ISSN, pp 2231\u20132307"},{"key":"4396_CR54","doi-asserted-by":"crossref","unstructured":"Li S, Hao F, Li M, Kim HC (2013) Medicine rating prediction and recommendation in mobile social networks. In: International conference on grid and pervasive computing, pp 216\u2013223. Springer","DOI":"10.1007\/978-3-642-38027-3_23"},{"key":"4396_CR55","unstructured":"Li S, Karatzoglou A, Gentile C: Collaborative filtering bandits. In: Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval"},{"issue":"2","key":"4396_CR56","first-page":"179","volume":"4","author":"X Li","year":"2001","unstructured":"Li X, Ye N (2001) Decision tree classifiers for computer intrusion detection. J Parallel Distrib Comput Pract 4(2):179\u2013190","journal-title":"J Parallel Distrib Comput Pract"},{"key":"4396_CR57","doi-asserted-by":"publisher","unstructured":"Lu C, Feng J, Lin Z, Mei T, Yan S (2018) Subspace clustering by block diagonal representation. IEEE Transactions on Pattern Analysis and Machine Intelligence pp 1\u20131. https:\/\/doi.org\/10.1109\/tpami.2018.2794348","DOI":"10.1109\/tpami.2018.2794348"},{"issue":"3","key":"4396_CR58","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1111\/j.0824-7935.2004.00247.x","volume":"20","author":"W Lu","year":"2004","unstructured":"Lu W, Traore I (2004) Detecting new forms of network intrusion using genetic programming. Comput Intell 20(3):475\u2013494","journal-title":"Comput Intell"},{"issue":"2","key":"4396_CR59","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/0005-2795(75)90109-9","volume":"405","author":"BW Matthews","year":"1975","unstructured":"Matthews BW (1975) Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure 405(2):442\u2013451","journal-title":"Biochimica et Biophysica Acta (BBA)-Protein Structure"},{"issue":"3","key":"4396_CR60","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s12065-013-0101-3","volume":"6","author":"M Mohammadi","year":"2014","unstructured":"Mohammadi M, Akbari A, Raahemi B, Nassersharif B, Asgharian H (2014) A fast anomaly detection system using probabilistic artificial immune algorithm capable of learning new attacks. Evol Intel 6(3):135\u2013156. https:\/\/doi.org\/10.1007\/s12065-013-0101-3","journal-title":"Evol Intel"},{"key":"4396_CR61","unstructured":"Moustafa JSN (2016) The unsw-nb15 data set description. https:\/\/www.unsw.adfa.edu.au\/australian-centre-for-cyber-security\/cybersecurity\/ADFA-NB15-Datasets\/"},{"key":"4396_CR62","doi-asserted-by":"publisher","unstructured":"Moustafa N, Slay J (2015) The significant features of the unsw-nb15 and the kdd99 data sets for network intrusion detection systems. Unpublished. https:\/\/doi.org\/10.13140\/RG.2.1.2264.4883","DOI":"10.13140\/RG.2.1.2264.4883"},{"key":"4396_CR63","doi-asserted-by":"publisher","unstructured":"Moustafa N, Slay J (2015) 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), pp 1\u20136. IEEE. https:\/\/doi.org\/10.1109\/milcis.2015.7348942","DOI":"10.1109\/milcis.2015.7348942"},{"key":"4396_CR64","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/19393555.2015.1125974","volume":"25","author":"N Moustafa","year":"2016","unstructured":"Moustafa N, Slay J (2016) 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 Global Perspect 25:1\u20133. https:\/\/doi.org\/10.1080\/19393555.2015.1125974","journal-title":"Inf Secur J Global Perspect"},{"key":"4396_CR65","unstructured":"Mukkamala S, Janoski G, Sung A (2002) Intrusion detection using neural networks and support vector machines. In: Neural Networks, 2002. IJCNN\u201902. In: Proceedings of the 2002 international joint conference on, vol\u00a02, pp 1702\u20131707. IEEE"},{"issue":"01","key":"4396_CR66","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 (2016) Evaluating feature selection methods for network intrusion detection with kyoto data. Int J Reliab Qual Saf Eng 23(01):1650001. https:\/\/doi.org\/10.1142\/s0218539316500017","journal-title":"Int J Reliab Qual Saf Eng"},{"key":"4396_CR67","doi-asserted-by":"publisher","unstructured":"Nastaiinullah, N., Adiwijaya, Kurniati, AP (2014) Anomaly detection on intrusion detection system using CLIQUE partitioning. In: 2014 2nd International conference on information and communication technology (ICoICT). IEEE. https:\/\/doi.org\/10.1109\/icoict.2014.6914031","DOI":"10.1109\/icoict.2014.6914031"},{"key":"4396_CR68","doi-asserted-by":"publisher","unstructured":"Nguyen HT, Petrovi\u0107 S, Franke K (2010) A comparison of feature-selection methods for intrusion detection, pp 242\u2013255. Springer. https:\/\/doi.org\/10.1007\/978-3-642-14706-7_19","DOI":"10.1007\/978-3-642-14706-7_19"},{"key":"4396_CR69","doi-asserted-by":"publisher","unstructured":"Noble CC, Cook DJ (2003) Graph-based anomaly detection. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM Press. https:\/\/doi.org\/10.1145\/956750.956831","DOI":"10.1145\/956750.956831"},{"key":"4396_CR70","volume-title":"Kuby immunology","author":"JA Owen","year":"2013","unstructured":"Owen JA, Punt J, Stranford SA et al (2013) Kuby immunology. WH Freeman, New York"},{"issue":"12","key":"4396_CR71","first-page":"258","volume":"7","author":"M Panda","year":"2007","unstructured":"Panda M, Patra MR (2007) Network intrusion detection using naive bayes. Int J Comput Sci Netw Secur 7(12):258\u2013263","journal-title":"Int J Comput Sci Netw Secur"},{"key":"4396_CR72","volume-title":"The immune system","author":"P Parham","year":"2015","unstructured":"Parham P (2015) The immune system, 4th edn. Garland Science, New York City","edition":"4"},{"key":"4396_CR73","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2007\u20132017) Scikit-learn tool. http:\/\/scikit-learn.org"},{"key":"4396_CR74","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"issue":"5","key":"4396_CR75","first-page":"660","volume":"19","author":"E Popoola","year":"2017","unstructured":"Popoola E, Adewumi AO (2017) Efficient feature selection technique for network intrusion detection system using discrete differential evolution and decision. IJ Netw Secur 19(5):660\u2013669","journal-title":"IJ Netw Secur"},{"key":"4396_CR76","unstructured":"Portnoy L (2000) Intrusion detection with unlabeled data using clustering"},{"key":"4396_CR77","doi-asserted-by":"crossref","unstructured":"Rathore H (2016) Mapping biological systems to network systems","DOI":"10.1007\/978-3-319-29782-8"},{"key":"4396_CR78","unstructured":"Ryan J, Lin MJ, Miikkulainen R (1998) Intrusion detection with neural networks. In: Proceedings of the advances in neural information processing systems 10: annual conference on neural information processing systems 1997, NeurIPS 1977, Denver, Colorado, USA, 1997. The MIT Press 1998, ISBN 0-262-10076-2"},{"issue":"1","key":"4396_CR79","doi-asserted-by":"crossref","first-page":"012016","DOI":"10.1088\/1757-899X\/173\/1\/012016","volume":"173","author":"T Salamatova","year":"2017","unstructured":"Salamatova T, Zhukov V (2017) Network intrusion detection by the coevolutionary immune algorithm of artificial immune systems with clonal selection. IOP Conf Ser Mater Sci Eng 173(1):012016","journal-title":"IOP Conf Ser Mater Sci Eng"},{"key":"4396_CR80","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.eswa.2016.03.042","volume":"60","author":"P Saurabh","year":"2016","unstructured":"Saurabh P, Verma B (2016) An efficient proactive artificial immune system based anomaly detection and prevention system. Expert Syst Appl 60:311\u2013320","journal-title":"Expert Syst Appl"},{"key":"4396_CR81","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.engappai.2014.06.022","volume":"35","author":"NA Seresht","year":"2014","unstructured":"Seresht NA, Azmi R (2014) MAIS-IDS: a distributed intrusion detection system using multi-agent ais approach. Eng Appl Artif Intell 35:286\u2013298","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"4396_CR82","first-page":"101","volume":"2","author":"R Shanmugavadivu","year":"2011","unstructured":"Shanmugavadivu R, Nagarajan N (2011) Network intrusion detection system using fuzzy logic. Indian J Comput Sci Eng (IJCSE) 2(1):101\u2013111","journal-title":"Indian J Comput Sci Eng (IJCSE)"},{"issue":"01","key":"4396_CR83","doi-asserted-by":"publisher","first-page":"41","DOI":"10.4236\/cn.2012.41006","volume":"04","author":"J Shen","year":"2012","unstructured":"Shen J, Wang J, Ai H (2012) An improved artificial immune system-based network intrusion detection by using rough set. CN 04(01):41\u201347. https:\/\/doi.org\/10.4236\/cn.2012.41006","journal-title":"CN"},{"issue":"18","key":"4396_CR84","doi-asserted-by":"crossref","first-page":"3799","DOI":"10.1016\/j.ins.2007.03.025","volume":"177","author":"T Shon","year":"2007","unstructured":"Shon T, Moon J (2007) A hybrid machine learning approach to network anomaly detection. Inf Sci 177(18):3799\u20133821","journal-title":"Inf Sci"},{"key":"4396_CR85","volume-title":"How the immune system works. The how it works series","author":"LM Sompayrac","year":"2016","unstructured":"Sompayrac LM (2016) How the immune system works. The how it works series, 5ed edn. Wiley, Hoboken","edition":"5ed"},{"key":"4396_CR86","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 first workshop on building analysis datasets and gathering experience returns for security, pp 29\u201336. ACM. https:\/\/doi.org\/10.1145\/1978672.1978676","DOI":"10.1145\/1978672.1978676"},{"key":"4396_CR87","unstructured":"Souici-Meslati L, Zekri M (2016) Immunological approach for intrusion detection. REVUE AFRICAINE DE LA RECHERCHE EN INFORMATIQUE ET MATH\u00c9MATIQUES APPLIQU\u00c9ES 17:"},{"key":"4396_CR88","unstructured":"Sridevi R, Chattemvelli R (2012) Genetic algorithm and artificial immune systems: a combinational approach for network intrusion detection. In: 2012 International Conference on Advances in Engineering, Science and Management (ICAESM), pp 494\u2013498. IEEE"},{"key":"4396_CR89","doi-asserted-by":"crossref","unstructured":"Tabatabaefar M, Miriestahbanati M, Gr\u00e9goire JC (2017) Network intrusion detection through artificial immune system. In: Systems Conference (SysCon), 2017 Annual IEEE International, pp 1\u20136. IEEE","DOI":"10.1109\/SYSCON.2017.7934751"},{"key":"4396_CR90","doi-asserted-by":"publisher","unstructured":"Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE symposium on computational intelligence for security and defense applications. Institute of Electrical & Electronics Engineers (IEEE). https:\/\/doi.org\/10.1109\/cisda.2009.5356528","DOI":"10.1109\/cisda.2009.5356528"},{"key":"4396_CR91","unstructured":"Traffic data from kyoto university\u2019s honeypots. http:\/\/www.takakura.com\/Kyoto_data\/data\/"},{"key":"4396_CR92","doi-asserted-by":"publisher","unstructured":"Vapnik VN (2000) The nature of statistical learning theory. Springer, New York. https:\/\/doi.org\/10.1007\/978-1-4757-3264-1","DOI":"10.1007\/978-1-4757-3264-1"},{"key":"4396_CR93","doi-asserted-by":"publisher","unstructured":"Xian JQ, Lang FH, Tang XL (2005) A novel intrusion detection method based on clonal selection clustering algorithm. In: 2005 International conference on machine learning and cybernetics, vol\u00a06, pp 3905\u20133910. IEEE. https:\/\/doi.org\/10.1109\/icmlc.2005.1527620","DOI":"10.1109\/icmlc.2005.1527620"},{"key":"4396_CR94","doi-asserted-by":"crossref","unstructured":"Yan Q, Yu J (2006) Ainids: an immune-based network intrusion detection system. In: Defense and security symposium, pp 62410U\u201362410U. International Society for Optics and Photonics","DOI":"10.1117\/12.664752"},{"key":"4396_CR95","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2014\/156790","volume":"2014","author":"H Yang","year":"2014","unstructured":"Yang H, Li T, Hu X, Wang F, Zou Y (2014) A survey of artificial immune system based intrusion detection. Sci World J 2014:1\u201311. https:\/\/doi.org\/10.1155\/2014\/156790","journal-title":"Sci World J"},{"key":"4396_CR96","first-page":"645","volume":"20","author":"H Yasir","year":"2018","unstructured":"Yasir H, Balasaraswathi VR, Journaux L, Sugumaran M (2018) Benchmark datasets for network intrusion detection: a review. Int J Netw Secur 20:645\u2013654","journal-title":"Int J Netw Secur"},{"key":"4396_CR97","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11042-015-3117-0","volume":"76","author":"C Yin","year":"2015","unstructured":"Yin C, Ma L, Feng L (2015) Towards accurate intrusion detection based on improved clonal selection algorithm. Multimed Tools Appl 76:1\u201314. https:\/\/doi.org\/10.1007\/s11042-015-3117-0","journal-title":"Multimed Tools Appl"},{"issue":"05","key":"4396_CR98","doi-asserted-by":"crossref","first-page":"1659013","DOI":"10.1142\/S0218001416590138","volume":"30","author":"C Yin","year":"2016","unstructured":"Yin C, Ma L, Feng L (2016) A feature selection method for improved clonal algorithm towards intrusion detection. Int J Pattern Recognit Artif Intell 30(05):1659013","journal-title":"Int J Pattern Recognit Artif Intell"},{"key":"4396_CR99","doi-asserted-by":"publisher","unstructured":"Zargari S, Voorhis D (2012) Feature selection in the corrected KDD-dataset. In: 2012 Third international conference on emerging intelligent data and web technologies. IEEE. https:\/\/doi.org\/10.1109\/eidwt.2012.10","DOI":"10.1109\/eidwt.2012.10"},{"issue":"2","key":"4396_CR100","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/S1005-8885(14)60290-9","volume":"21","author":"L Zhang","year":"2014","unstructured":"Zhang L, ying BAI Z, long LU Y, xing ZHA Y, wen LI Z (2014) Integrated intrusion detection model based on artificial immune. J China Univ Posts Telecommun 21(2):83\u201390","journal-title":"J China Univ Posts Telecommun"},{"key":"4396_CR101","doi-asserted-by":"publisher","unstructured":"Zhao X, Wang G, Li Z (2016) Unsupervised network anomaly detection based on abnormality weights and subspace clustering. In: 2016 Sixth international conference on information science and technology (ICIST). IEEE. https:\/\/doi.org\/10.1109\/icist.2016.7483462","DOI":"10.1109\/icist.2016.7483462"},{"key":"4396_CR102","unstructured":"Zhu X (2005) Semi-supervised learning literature survey. Technical Report 1530, Department of Computer Sciences, University of Wosconsin, Madison"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04396-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-019-04396-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04396-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T07:13:01Z","timestamp":1664089981000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-019-04396-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,6]]},"references-count":102,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["4396"],"URL":"https:\/\/doi.org\/10.1007\/s00521-019-04396-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,6]]},"assertion":[{"value":"19 January 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2019","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":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}