{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T01:51:24Z","timestamp":1780624284962,"version":"3.54.1"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2020,12,5]],"date-time":"2020-12-05T00:00:00Z","timestamp":1607126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The dendritic cell algorithm (DCA) as one of the emerging evolutionary algorithms is based on the behavior of the specific immune agents, known as dendritic cells (DCs). DCA has several potentially beneficial features for binary classification problems. In this paper, we aim at providing a new version of this immune-inspired mechanism acts as a semi-supervised classifier, which can be a defensive shield in network intrusion detection problem. Till now, no strategy or idea has been adopted on the $Get_{Antigen()}$ function on the detection phase, but random sampling entails the DCA to provide undesirable results in several cycles at each time. This leads to uncertainty. Whereas it must be accomplished by biological behaviors of DCs in peripheral tissues, we have proposed a novel strategy that exactly acts based on its immunological functionalities of dendritic cells. The proposed mechanism focuses on two items: first, to obviate the challenge of needing to have a preordered antigen set for computing danger signal, and the second, to provide a novel immune-inspired idea for nonrandom data sampling. A variable functional migration threshold is also computed cycle by cycle that shows the necessity of the migration threshold flexibility. A significant criterion so-called capability of intrusion detection (CID) is used for tests. All the tests have been performed in a new benchmark dataset named UNSW-NB15. Experimental consequences demonstrate that the present schema as the best version among improved DC algorithms achieves 76.69% CID by 90% accuracy and outperforms its counterpart methods.<\/jats:p>","DOI":"10.1093\/comjnl\/bxaa140","type":"journal-article","created":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T03:19:40Z","timestamp":1601522380000},"page":"1193-1214","source":"Crossref","is-referenced-by-count":14,"title":["A New Intrusion Detection System Using the Improved Dendritic Cell Algorithm"],"prefix":"10.1093","volume":"64","author":[{"given":"Ehsan","family":"Farzadnia","sequence":"first","affiliation":[{"name":"Faculty of Electrical and Computer Engineering, Malek Ashtar University of Technology, Tehran 15875-1774, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hossein","family":"Shirazi","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Computer Engineering, Malek Ashtar University of Technology, Tehran 15875-1774, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alireza","family":"Nowroozi","sequence":"additional","affiliation":[{"name":"Media Engineering Department, IRIB University, Tehran 1995614317, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,12,5]]},"reference":[{"key":"2021082512504524100_ref1","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1016\/j.asoc.2017.06.059","article-title":"A novel hybridization strategy for krill herd algorithm applied to clustering techniques","volume":"60","author":"Abualigah Khader","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"2021082512504524100_ref2","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.engappai.2018.05.003","article-title":"A combination of objective functions and hybrid krill herd algorithm for text document clustering analysis","volume":"73","author":"Abualigah","year":"2018","journal-title":"Eng. Appl. Artif. Intel."},{"key":"2021082512504524100_ref3","doi-asserted-by":"publisher","first-page":"4047","DOI":"10.1007\/s10489-018-1190-6","article-title":"Hybrid clustering analysis using improved krill herd algorithm","volume":"48","author":"Abualigah","year":"2018","journal-title":"Appl. Intell."},{"key":"2021082512504524100_ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-10674-4","volume-title":"Feature selection and enhanced krill herd algorithm for text document clustering","author":"Abualigah","year":"2019"},{"issue":"11","key":"2021082512504524100_ref5","doi-asserted-by":"publisher","first-page":"4773","DOI":"10.1007\/s11227-017-2046-2","article-title":"Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering","volume":"73","author":"Abualigah","year":"2017","journal-title":"J. Supercomput."},{"key":"2021082512504524100_ref6","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1016\/j.jocs.2017.07.018","article-title":"A new feature selection method to improve the document clustering using particle swarm optimization algorithm","volume":"25","author":"Abualigah","year":"2018","journal-title":"J. Comput. Sci."},{"key":"2021082512504524100_ref7","doi-asserted-by":"publisher","first-page":"20281","DOI":"10.1109\/ACCESS.2019.2897580","article-title":"An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem","volume":"7","author":"Deng","year":"2019","journal-title":"IEEE Access"},{"key":"2021082512504524100_ref8","doi-asserted-by":"publisher","first-page":"4387","DOI":"10.1007\/s00500-016-2071-8","article-title":"A novel collaborative optimization algorithm in solving complex optimization problems","volume":"21","author":"Deng","year":"2017","journal-title":"Soft Compu."},{"key":"2021082512504524100_ref9","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/j.asoc.2017.06.004","article-title":"Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment","volume":"59","author":"Deng","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"2021082512504524100_ref10","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1016\/j.ins.2014.03.128","article-title":"Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm","volume":"279","author":"Arqub","year":"2014","journal-title":"Inform. Sci."},{"key":"2021082512504524100_ref11","doi-asserted-by":"publisher","first-page":"3283","DOI":"10.1007\/s00500-015-1707-4","article-title":"Numerical solutions of fuzzy differential equations using reproducing kernel Hilbert space method","volume":"20","author":"Arqub","year":"2016","journal-title":"Soft Compu."},{"key":"2021082512504524100_ref12","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1007\/978-3-030-29516-5_27","volume-title":"Immune Inspired Dendritic Cell Algorithm for Stock Price Manipulation Detection. In Proceedings of SAI Intelligent Systems Conference","author":"Rizvi","year":"2019"},{"key":"2021082512504524100_ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IWCMC.2019.8766461","volume-title":"2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 877\u2013882. IEEE","author":"Almasalmeh","year":"2019"},{"key":"2021082512504524100_ref14","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/978-3-030-29933-0_44","volume-title":"Signal Categorisation for Dendritic Cell Algorithm Using GA with Partial Shuffle Mutation. In UK Workshop on Computational Intelligence","author":"Noe","year":"2019"},{"key":"2021082512504524100_ref15","first-page":"1","article-title":"Dendritic cell algorithm enhancement using fuzzy inference system for network intrusion detection","author":"Noe","year":"2019"},{"key":"2021082512504524100_ref16","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.ins.2012.08.023","article-title":"Black hole: A new heuristic optimization approach for data clustering","volume":"222","author":"","year":"2013","journal-title":"Inform. Sci."},{"key":"2021082512504524100_ref17","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/978-3-319-56154-7_19","article-title":"Cyber immunity a bio-inspired cyber defense system","author":"Wlodarczak","year":"2017"},{"key":"2021082512504524100_ref18","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1007\/s10898-011-9736-8","article-title":"Clonal selection: An immunological algorithm for global optimization over continuous spaces","volume":"53","author":"Pavone","year":"2012","journal-title":"J Global Optimization"},{"key":"2021082512504524100_ref19","author":"Santanelli","year":"2016"},{"key":"2021082512504524100_ref20","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1007\/s10115-015-0891-y","article-title":"A survey of the dendritic cell algorithm","volume":"48","author":"Chelly","year":"2016","journal-title":"Knowl. Inform. Sys."},{"key":"2021082512504524100_ref21","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/978-3-540-85072-4_26","volume-title":"International conference on artificial immune systems","author":"Greensmith","year":"2010"},{"key":"2021082512504524100_ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CISIM.2008.31","volume-title":"Evolution induced secondary immunity: An artificial immune system based intrusion detection system, in: Computer information systems and industrial management applications","author":"Dal","year":"2008"},{"key":"2021082512504524100_ref23","volume-title":"The dendritic cell algorithm (Ph.D. thesis edn)","author":"Greensmith","year":"2007"},{"key":"2021082512504524100_ref24","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1109\/BADGERS.2015.014","volume-title":"The significant features of the unsw-nb15 and the kdd99 data sets for network intrusion detection systems, in: Building analysis datasets a nd gathering experience returns for security(BADGERS), 4th international workshop","author":"Moustafa","year":"2015"},{"key":"2021082512504524100_ref25","first-page":"67","article-title":"Cloudids: Cloud intrusion detec- tion model inspired by dendritic cell mechanism","volume":"9","author":"Azuan Ahmad","year":"2017","journal-title":"Int. J. Commun. Networks Inform. Security"},{"key":"2021082512504524100_ref26","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1016\/j.procs.2017.12.204","article-title":"A study on intrusion detection using centroid-based classification","volume":"124","author":"Setiawan","year":"2017","journal-title":"Proc. Comput. Sci."},{"key":"2021082512504524100_ref27","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1145\/1128817.1128834","article-title":"Measuring intrusion detection capability: An information theoretic approach","author":"Gu","year":"2006"},{"issue":"11","key":"2021082512504524100_ref28","first-page":"11","article-title":"Evaluation metrics for intrusion detection systems-a study","volume":"2","author":"Kumar","year":"2014","journal-title":"Int. J.f Comput. Sci. Mobile Appl."},{"key":"2021082512504524100_ref29","doi-asserted-by":"publisher","first-page":"2670","DOI":"10.1016\/j.eswa.2014.11.009","article-title":"A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems","volume":"42","author":"Eesa","year":"2015","journal-title":"Expert Sys. Appl."},{"key":"2021082512504524100_ref30","volume-title":"A scalable and distributed dendritic cell algorithm for big data classification. Swarm Evolut. Comput","author":"Chelly","year":"2018"},{"key":"2021082512504524100_ref31","volume-title":"2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1\u20138. IEEE","author":"Elisa","year":"2018"},{"key":"2021082512504524100_ref32","volume-title":"University of Nottingham","author":"Gu","year":"2011"},{"key":"2021082512504524100_ref33","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/978-3-319-97982-3_17","volume-title":"Dendritic cell algorithm with fuzzy inference system for input signal generation. In UK workshop on computational intelligence","author":"Noe","year":"2018"},{"key":"2021082512504524100_ref34","doi-asserted-by":"publisher","DOI":"10.1109\/HPCC\/SmartCity\/DSS.2018.00254","article-title":"A revised dendritic cell algorithm using k-means clustering","volume-title":"2018 IEEE 20th international conference on high performance computing and communications; IEEE 16th international conference on Smart City; IEEE 4th international conference on data science and systems (HPCC\/SmartCity\/DSS), pp. 1547\u20131554. IEEE","author":"E., Noe, L. Yang, Yanpeng Q., and F. Chao","year":"2018"},{"key":"2021082512504524100_ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3205651.3205701","article-title":"A distributed dendritic cell algorithm for big data","volume-title":"proceedings of the genetic and evolutionary computation conference companion, pp. 103\u2013104. ACM","author":"Dagdia","year":"2018"},{"key":"2021082512504524100_ref36","doi-asserted-by":"publisher","first-page":"682","DOI":"10.3390\/e20090682","article-title":"Study on a novel fault damage degree identification method using high-order differential mathematical morphology gradient spectrum entropy","volume":"20","author":"Zhao","year":"2018","journal-title":"Entropy"},{"key":"2021082512504524100_ref37","doi-asserted-by":"publisher","first-page":"99263","DOI":"10.1109\/ACCESS.2019.2929094","article-title":"Fault diagnosis method based on principal component analysis and broad learning system","volume":"7","author":"Zhao","year":"2019","journal-title":"IEEE Access"}],"container-title":["The Computer Journal"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/comjnl\/article-pdf\/64\/8\/1193\/39904349\/bxaa140.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/comjnl\/article-pdf\/64\/8\/1193\/39904349\/bxaa140.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,25]],"date-time":"2021-08-25T12:51:02Z","timestamp":1629895862000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/comjnl\/article\/64\/8\/1193\/6015901"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,5]]},"references-count":37,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,12,5]]},"published-print":{"date-parts":[[2021,8,25]]}},"URL":"https:\/\/doi.org\/10.1093\/comjnl\/bxaa140","relation":{},"ISSN":["0010-4620","1460-2067"],"issn-type":[{"value":"0010-4620","type":"print"},{"value":"1460-2067","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,8]]},"published":{"date-parts":[[2020,12,5]]}}}