{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T06:43:18Z","timestamp":1792132998955,"version":"4.4.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T00:00:00Z","timestamp":1568937600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T00:00:00Z","timestamp":1568937600000},"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":["Evol. Intel."],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s12065-019-00291-w","type":"journal-article","created":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T17:15:01Z","timestamp":1568999701000},"page":"47-59","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["UIDS: a unified intrusion detection system for IoT environment"],"prefix":"10.1007","volume":"14","author":[{"given":"Vikash","family":"Kumar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayan Kumar","family":"Das","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ditipriya","family":"Sinha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,9,20]]},"reference":[{"issue":"1","key":"291_CR1","first-page":"2230","volume":"2","author":"Z Malek","year":"2013","unstructured":"Malek Z, Trivedi B (2013) A study of anomaly intrusion detection using machine learning techniques. Int J Enterp Comput Bus Syst 2(1):2230\u20138849","journal-title":"Int J Enterp Comput Bus Syst"},{"issue":"11","key":"291_CR2","first-page":"252","volume":"7","author":"A Haroon","year":"2016","unstructured":"Haroon A, Shah MA, Asim Y, Naeem W, Kamran M, Javaid Q (2016) Constraints in the IoT: the world in 2020 and beyond. Constraints J 7(11):252","journal-title":"Constraints J"},{"key":"291_CR3","doi-asserted-by":"crossref","unstructured":"Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the KDD CUP 99 data set. In: Computational intelligence for security and defense applications, 2009. CISDA 2009. IEEE Symposium on:1\u20136","DOI":"10.1109\/CISDA.2009.5356528"},{"issue":"12","key":"291_CR4","first-page":"1848","volume":"2","author":"S Revathi","year":"2013","unstructured":"Revathi S, Malathi A (2013) A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection. Int J Eng Res Technol 2(12):1848\u20131853","journal-title":"Int J Eng Res Technol"},{"key":"291_CR5","doi-asserted-by":"crossref","unstructured":"Moustafa N, Slay J (2015) UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: Military communications and information systems conference (MilCIS), 2015. IEEE:1\u20136","DOI":"10.1109\/MilCIS.2015.7348942"},{"issue":"1\u20133","key":"291_CR6","doi-asserted-by":"publisher","first-page":"18","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 dataset. Inf Secur J A Glob Perspect 25(1\u20133):18\u201331","journal-title":"Inf Secur J A Glob Perspect"},{"key":"291_CR7","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1016\/j.future.2017.09.056","volume":"79","author":"D Papamartzivanos","year":"2018","unstructured":"Papamartzivanos D, M\u00e1rmol FG, Kambourakis G (2018) Dendron: genetic trees driven rule induction for network intrusion detection systems. Future Gener Comput Syst 79:558\u2013574","journal-title":"Future Gener Comput Syst"},{"key":"291_CR8","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jnca.2017.01.033","volume":"83","author":"M Ge","year":"2017","unstructured":"Ge M, Hong JB, Guttmann W, Kim DS (2017) A framework for automating security analysis of the internet of things. J Netw Comput Appl 83:12\u201327","journal-title":"J Netw Comput Appl"},{"issue":"7","key":"291_CR9","first-page":"13469","volume":"5","author":"TM Mehare","year":"2017","unstructured":"Mehare TM, Bhosale S (2017) Design and development of intrusion detection system for internet of things. Int J Innov Res Comput Commun Eng 5(7):13469\u201313475","journal-title":"Int J Innov Res Comput Commun Eng"},{"key":"291_CR10","doi-asserted-by":"crossref","unstructured":"Hodo E, Bellekens X, Hamilton A, Dubouilh PL, Iorkyase E, Tachtatzis C, Atkinson R (2016) Threat analysis of IoT networks using artificial neural network intrusion detection system. In: 2016 international symposium on networks, computers and communications (ISNCC). IEEE, 1\u20136","DOI":"10.1109\/ISNCC.2016.7746067"},{"key":"291_CR11","unstructured":"Koroniotis N, Moustafa N, Sitnikova E, Slay J (2017) Towards developing network forensic mechanism for botnet activities in the IoT based on machine learning techniques. In: international conference on mobile networks and management, Springer Cham:30\u201344"},{"key":"291_CR12","unstructured":"Jha J, Ragha L (2013) Intrusion detection system using support vector machine. In: International Journal of Applied Information Systems: Proceedings on International Conference and workshop on Advanced Computing ICWAC, vol 3, Foundation of Computer Science, New York, USA, pp 25\u201330"},{"issue":"3","key":"291_CR13","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. Evolut Intel 6(3):135\u2013156","journal-title":"Evolut Intel"},{"key":"291_CR14","unstructured":"Chowdhury MN, Ferens K, Ferens M (2016) Network Intrusion Detection Using Machine Learning. In: Proceedings of the International Conference on Security and Management (SAM):30"},{"key":"291_CR15","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.inffus.2016.04.007","volume":"33","author":"HH Bosman","year":"2017","unstructured":"Bosman HH, Iacca G, Tejada A, W\u00f6rtche HJ, Liotta A (2017) Spatial anomaly detection in sensor networks using neighborhood information. Information Fusion 33:41\u201356","journal-title":"Information Fusion"},{"issue":"1","key":"291_CR16","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/s11277-015-2734-2","volume":"85","author":"F Hidoussi","year":"2015","unstructured":"Hidoussi F, Toral-Cruz H, Boubiche DE, Lakhtaria K, Mihovska A, Voznak M (2015) Centralized IDS based on misuse detection for cluster-based wireless sensors networks. Wireless Pers Commun 85(1):207\u2013224","journal-title":"Wireless Pers Commun"},{"issue":"6","key":"291_CR17","doi-asserted-by":"publisher","first-page":"339","DOI":"10.14257\/ijfgcn.2016.9.6.32","volume":"9","author":"SK Patel","year":"2016","unstructured":"Patel SK, Sonker A (2016) Rule-based network intrusion detection system for port scanning with efficient port scan detection rules using snort. International Journal of Future Generation Communication and Networking 9(6):339\u2013350","journal-title":"International Journal of Future Generation Communication and Networking"},{"issue":"2","key":"291_CR18","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s12065-019-00199-5","volume":"12","author":"I Benmessahel","year":"2019","unstructured":"Benmessahel I, Xie K, Chellal M, Semong T (2019) A new evolutionary neural networks based on intrusion detection systems using locust swarm optimization. Evol Intell 12(2):131\u2013146","journal-title":"Evol Intell"},{"issue":"10","key":"291_CR19","doi-asserted-by":"publisher","first-page":"18249","DOI":"10.18535\/ijecs\/v5i10.05","volume":"5","author":"P Akshaya","year":"2016","unstructured":"Akshaya P (2016) Intrusion detection system using machine learning approach. Int J Eng Comput Sci 5(10):18249\u201318254. https:\/\/doi.org\/10.18535\/ijecs\/v5i10.05","journal-title":"Int J Eng Comput Sci"},{"issue":"6","key":"291_CR20","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1007\/s13042-014-0309-2","volume":"7","author":"M Agarwal","year":"2016","unstructured":"Agarwal M, Pasumarthi D, Biswas S, Nandi S (2016) Machine learning approach for detection of flooding DoS attacks in 802.11 networks and attacker localization. Int J Mach Learn Cybern 7(6):1035\u20131051. https:\/\/doi.org\/10.1007\/s13042-014-0309-2","journal-title":"Int J Mach Learn Cybern"},{"issue":"16","key":"291_CR21","doi-asserted-by":"publisher","first-page":"6391","DOI":"10.1007\/s11042-014-2097-9","volume":"74","author":"N Wattanapongsakorn","year":"2015","unstructured":"Wattanapongsakorn N, Charnsripinyo C (2015) Web-based monitoring approach for network-based intrusion detection and prevention. Multimed Tools Appl 74(16):6391\u20136411. https:\/\/doi.org\/10.1007\/s11042-014-2097-9","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"291_CR22","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1109\/TSMCC.2010.2050685","volume":"41","author":"S Mabu","year":"2011","unstructured":"Mabu S, Chen C, Lu N, Shimada K, Hirasawa K (2011) An intrusion-detection model based on fuzzy class-association-rule mining using genetic network programming. IEEE Trans Syst Man Cybern Part C Appl Rev 41(1):130\u2013139","journal-title":"IEEE Trans Syst Man Cybern Part C Appl Rev"},{"issue":"1","key":"291_CR23","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.eswa.2014.08.002","volume":"42","author":"S Elhag","year":"2015","unstructured":"Elhag S, Fern\u00e1ndez A, Bawakid A, Alshomrani S, Herrera F (2015) On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on intrusion detection systems. Expert Syst Appl 42(1):193\u2013202","journal-title":"Expert Syst Appl"},{"issue":"1","key":"291_CR24","doi-asserted-by":"publisher","first-page":"17","DOI":"10.5121\/ijcsit.2016.8102","volume":"8","author":"HPS Sasan","year":"2016","unstructured":"Sasan HPS, Sharma M (2016) Intrusion detection using feature selection and machine learning algorithm with misuse detection. Int J Comput Sci Inf Technol (IJCSIT) 8(1):17. https:\/\/doi.org\/10.5121\/ijcsit.2016.8102","journal-title":"Int J Comput Sci Inf Technol (IJCSIT)"},{"key":"291_CR25","unstructured":"Weka 3.6.0 tools. http:\/\/www.cs.waikato.ac.nz\/ml\/weka\/"},{"issue":"4","key":"291_CR26","doi-asserted-by":"publisher","first-page":"1937","DOI":"10.1016\/j.eswa.2013.08.089","volume":"41","author":"DM Farid","year":"2014","unstructured":"Farid DM, Zhang L, Rahman CM, Hossain MA, Strachan R (2014) Hybrid decision tree and na\u00efve Bayes classifiers for multi-class classification tasks. Expert Syst Appl 41(4):1937\u20131946","journal-title":"Expert Syst Appl"},{"issue":"7","key":"291_CR27","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1007\/s12046-018-0910-2","volume":"43","author":"C Modi","year":"2018","unstructured":"Modi C, Patel D (2018) A feasible approach to intrusion detection in virtual network layer of cloud computing. S\u0101dhan\u0101 43(7):114","journal-title":"S\u0101dhan\u0101"},{"issue":"8","key":"291_CR28","doi-asserted-by":"publisher","first-page":"2661","DOI":"10.1016\/j.adhoc.2013.04.014","volume":"11","author":"S Raza","year":"2013","unstructured":"Raza S, Wallgren L, Voigt T (2013) SVELTE: real-time intrusion detection in the Internet of Things. Ad Hoc Netw 11(8):2661\u20132674","journal-title":"Ad Hoc Netw"},{"issue":"10","key":"291_CR29","doi-asserted-by":"publisher","first-page":"5156","DOI":"10.1007\/s11227-018-2413-7","volume":"74","author":"A Mehmood","year":"2018","unstructured":"Mehmood A, Mukherjee M, Ahmed SH, Song H, Malik KM (2018) NBC-MAIDS: na\u00efve Bayesian classification technique in multi-agent system-enriched IDS for securing IoT against DDoS attacks. J Supercomput 74(10):5156\u20135170","journal-title":"J Supercomput"},{"key":"291_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s12065-019-00244-3","author":"QS Penukonda","year":"2019","unstructured":"Penukonda QS, Paramasivam I (2019) Design and analysis of behaviour based DDoS detection algorithm for data centres in cloud. Evolut Intell. https:\/\/doi.org\/10.1007\/s12065-019-00244-3","journal-title":"Evolut Intell"},{"issue":"25","key":"291_CR31","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.jocs.2017.03.006","volume":"1","author":"S Aljawarneh","year":"2018","unstructured":"Aljawarneh S, Aldwairi M, Yassein MB (2018) Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. J Comput Sci 1(25):152\u2013160","journal-title":"J Comput Sci"},{"issue":"98","key":"291_CR32","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.comcom.2016.12.001","volume":"15","author":"H Bostani","year":"2017","unstructured":"Bostani H, Sheikhan M (2017) Hybrid of anomaly-based and specification-based IDS for Internet of Things using unsupervised OPF based on MapReduce approach. Comput Commun 15(98):52\u201371","journal-title":"Comput Commun"},{"issue":"82","key":"291_CR33","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1016\/j.future.2017.08.043","volume":"1","author":"AA Diro","year":"2018","unstructured":"Diro AA, Chilamkurti N (2018) Distributed attack detection scheme using deep learning approach for Internet of Things. Future Gener Comput Syst 1(82):761\u2013768","journal-title":"Future Gener Comput Syst"}],"container-title":["Evolutionary Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-019-00291-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12065-019-00291-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-019-00291-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,22]],"date-time":"2021-03-22T06:29:39Z","timestamp":1616394579000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12065-019-00291-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,20]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["291"],"URL":"https:\/\/doi.org\/10.1007\/s12065-019-00291-w","relation":{},"ISSN":["1864-5909","1864-5917"],"issn-type":[{"value":"1864-5909","type":"print"},{"value":"1864-5917","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,20]]},"assertion":[{"value":"31 January 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 September 2019","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2019","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}