{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T14:31:04Z","timestamp":1762353064626,"version":"3.37.3"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T00:00:00Z","timestamp":1619395200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T00:00:00Z","timestamp":1619395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672338","61873160"],"award-info":[{"award-number":["61672338","61873160"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1007\/s00500-021-05809-y","type":"journal-article","created":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T02:02:22Z","timestamp":1619402542000},"page":"10935-10951","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A two-stage intrusion detection approach for software-defined IoT networks"],"prefix":"10.1007","volume":"25","author":[{"given":"Qiuting","family":"Tian","sequence":"first","affiliation":[]},{"given":"Dezhi","family":"Han","sequence":"additional","affiliation":[]},{"given":"Meng-Yen","family":"Hsieh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1381-4364","authenticated-orcid":false,"given":"Kuan-Ching","family":"Li","sequence":"additional","affiliation":[]},{"given":"Arcangelo","family":"Castiglione","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,26]]},"reference":[{"issue":"10","key":"5809_CR1","first-page":"806954","volume":"11","author":"I Ahmad","year":"2015","unstructured":"Ahmad I (2015) Feature selection using particle swarm optimization in intrusion detection. Int J Distrib Sens Netw 11(10):806954","journal-title":"Int J Distrib Sens Netw"},{"issue":"2018","key":"5809_CR2","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1016\/j.measurement.2018.07.067","volume":"129","author":"AH Alavi","year":"2018","unstructured":"Alavi AH, Jiao P, Buttlar WG et al (2018) Internet of Things-enabled smart cities: state-of-the-art and future trends. Measurement 129(2018):589\u2013606","journal-title":"Measurement"},{"key":"5809_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113249","author":"H Alazzam","year":"2020","unstructured":"Alazzam H, Sharieh A, Sabri KE et al (2020) A feature selection algorithm for intrusion detection system based on Pigeon Inspired Optimizer. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2020.113249","journal-title":"Expert Syst Appl"},{"issue":"6","key":"5809_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/sym12061046","volume":"12","author":"O Almomani","year":"2020","unstructured":"Almomani O (2020) A feature selection model for network intrusion detection system based on PSO, GWO, FFA and GA algorithms. Symmetry 12(6):1\u201320","journal-title":"Symmetry"},{"key":"5809_CR5","doi-asserted-by":"crossref","unstructured":"Bull P, Austin R, Popov E et al (2016) Flow based security for IoT devices using an SDN gateway. In: Conference on the future of the internet, 2016, pp 157\u2013163","DOI":"10.1109\/FiCloud.2016.30"},{"key":"5809_CR6","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"issue":"5","key":"5809_CR7","doi-asserted-by":"publisher","first-page":"9076","DOI":"10.1109\/JIOT.2019.2927497","volume":"6","author":"M Cui","year":"2019","unstructured":"Cui M, Han D, Wang J (2019) An efficient and safe road condition monitoring authentication scheme based on fog computing. IEEE Internet Things J 6(5):9076\u20139084","journal-title":"IEEE Internet Things J"},{"issue":"12","key":"5809_CR8","doi-asserted-by":"publisher","first-page":"15815","DOI":"10.1109\/TVT.2020.3036631","volume":"69","author":"M Cui","year":"2020","unstructured":"Cui M, Han D, Wang J et al (2020) ARFV: an efficient shared data auditing scheme supporting revocation for fog-assisted vehicular ad-hoc networks. IEEE Trans Veh Technol 69(12):15815\u201315827","journal-title":"IEEE Trans Veh Technol"},{"issue":"2016","key":"5809_CR9","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.neucom.2015.01.106","volume":"188","author":"Y Ding","year":"2016","unstructured":"Ding Y, Fu X (2016) Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm. Neurocomputing 188(2016):233\u2013238","journal-title":"Neurocomputing"},{"key":"5809_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2020.2977646","author":"D Han","year":"2020","unstructured":"Han D, Pan N, Li KC (2020) A traceable and revocable ciphertext-policy attribute-based encryption scheme based on privacy protection[J]. IEEE Trans Dependable Secure Comput. https:\/\/doi.org\/10.1109\/TDSC.2020.2977646","journal-title":"IEEE Trans Dependable Secure Comput"},{"key":"5809_CR11","doi-asserted-by":"publisher","unstructured":"Han D, Pan N, Li K (forthcoming) A traceable and revocable ciphertext-policy attribute-based encryption scheme based on privacy protection. In: IEEE transactions on dependable and secure computing. IEEE. https:\/\/doi.org\/10.1109\/TDSC.2020.2977646","DOI":"10.1109\/TDSC.2020.2977646"},{"issue":"1","key":"5809_CR12","first-page":"49","volume":"8","author":"A Khadwilard","year":"2012","unstructured":"Khadwilard A, Chansombat S, Thepphakorn T et al (2012) Application of firefly algorithm and its parameter setting for job shop scheduling. J Ind Technol 8(1):49\u201358","journal-title":"J Ind Technol"},{"issue":"2017","key":"5809_CR13","doi-asserted-by":"publisher","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(2017):255\u2013277","journal-title":"Comput Secur"},{"issue":"2020","key":"5809_CR14","first-page":"1","volume":"172","author":"C Khammassi","year":"2020","unstructured":"Khammassi C, Krichen S (2020) A NSGA2-LR wrapper approach for feature selection in network intrusion detection. Comput Netw 172(2020):1\u201318","journal-title":"Comput Netw"},{"issue":"2015","key":"5809_CR15","first-page":"2542","volume":"3","author":"Y Li","year":"2015","unstructured":"Li Y, Chen M (2015) Software-defined network function virtualization: a survey. IEEE Access 3(2015):2542\u20132553","journal-title":"IEEE Access"},{"key":"5809_CR16","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2020.3009447","author":"H Li","year":"2020","unstructured":"Li H, Han D, Tang M (2020) A privacy-preserving charging scheme for electric vehicles using blockchain and fog computing. IEEE Syst J. https:\/\/doi.org\/10.1109\/JSYST.2020.3009447","journal-title":"IEEE Syst J"},{"issue":"2","key":"5809_CR17","doi-asserted-by":"publisher","first-page":"2093","DOI":"10.1109\/JIOT.2018.2883344","volume":"6","author":"J Li","year":"2019","unstructured":"Li J, Zhao Z, Li R et al (2019) AI-based two-stage intrusion detection for software defined IoT networks. IEEE Internet Things J 6(2):2093\u20132102","journal-title":"IEEE Internet Things J"},{"issue":"7","key":"5809_CR18","doi-asserted-by":"publisher","first-page":"6392","DOI":"10.1109\/JIOT.2020.2974281","volume":"7","author":"W Liang","year":"2020","unstructured":"Liang W, Huang W, Long J, Zhang K, Li K-C, Zhang D (2020) Deep reinforcement learning for resource protection and real-time detection in IoT environment. IEEE Internet Things J 7(7):6392\u20136401","journal-title":"IEEE Internet Things J"},{"issue":"3","key":"5809_CR19","doi-asserted-by":"publisher","first-page":"2063","DOI":"10.1109\/TII.2019.2946791","volume":"16","author":"W Liang","year":"2019","unstructured":"Liang W, Li K-C, Long J, Kui X, Zomaya AY (2019) An industrial network intrusion detection algorithm based on multifeature data clustering optimization model. IEEE Trans Ind Inf 16(3):2063\u20132071","journal-title":"IEEE Trans Ind Inf"},{"issue":"8","key":"5809_CR20","doi-asserted-by":"publisher","first-page":"3210","DOI":"10.1007\/s11227-016-1631-0","volume":"72","author":"KC Lin","year":"2016","unstructured":"Lin KC, Zhang KY, Huang YH et al (2016) Feature selection based on an improved cat swarm optimization algorithm for big data classification. J Supercomput 72(8):3210\u20133221","journal-title":"J Supercomput"},{"issue":"1","key":"5809_CR21","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/JIOT.2017.2779180","volume":"5","author":"Y Liu","year":"2018","unstructured":"Liu Y, Kuang Y, Xiao Y et al (2018) SDN-based data transfer security for Internet of Things. IEEE Internet Things J 5(1):257\u2013268","journal-title":"IEEE Internet Things J"},{"issue":"21","key":"5809_CR22","doi-asserted-by":"publisher","first-page":"8221","DOI":"10.1016\/j.eswa.2015.06.024","volume":"42","author":"NC Long","year":"2015","unstructured":"Long NC, Meesad P, Unger H (2015) A highly accurate firefly based algorithm for heart disease prediction. Expert Syst Appl 42(21):8221\u20138231","journal-title":"Expert Syst Appl"},{"issue":"1","key":"5809_CR23","first-page":"68","volume":"5","author":"Z Ma\u0161etic","year":"2016","unstructured":"Ma\u0161etic Z, Subasi A, Azemovic J (2016) Malicious web sites detection using C4. 5 decision tree. Southeast Eur J Soft Comput 5(1):68\u201372","journal-title":"Southeast Eur J Soft Comput"},{"issue":"4","key":"5809_CR24","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1080\/18756891.2013.802114","volume":"6","author":"Y Meng","year":"2013","unstructured":"Meng Y, Kwok LF (2013) Enhancing false alarm reduction using voted ensemble selection in intrusion detection. Int J Comput Intell Syst 6(4):626\u2013638","journal-title":"Int J Comput Intell Syst"},{"key":"5809_CR25","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: Proceedings of the 2015 military communications and information systems conference (MilCIS). IEEE, pp 1\u20136","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"5809_CR26","doi-asserted-by":"crossref","unstructured":"Nastic S, Sehic S, Le DH et al (2014) Provisioning software-defined IoT cloud systems. In: Proceedings of the 2014 international conference on future internet of things and cloud. IEEE, pp 288\u2013295","DOI":"10.1109\/FiCloud.2014.52"},{"issue":"3","key":"5809_CR27","first-page":"335","volume":"48","author":"X Niu","year":"2014","unstructured":"Niu X (2014) Support vector extracted algorithm based on KNN and 10 fold cross-validation method. J Huazhong Normal Univ 48(3):335","journal-title":"J Huazhong Normal Univ"},{"key":"5809_CR28","doi-asserted-by":"crossref","unstructured":"Nobakht M, Sivaraman V, Boreli R, et al. A Host-Based Intrusion Detection and Mitigation Framework for Smart Home IoT Using OpenFlow[C]. availability, reliability and security, 2016: 147\u2013156.","DOI":"10.1109\/ARES.2016.64"},{"key":"5809_CR29","doi-asserted-by":"crossref","unstructured":"Nyga D, Picklum M, Koralewski S et al (2017) Instruction completion through instance-based learning and semantic analogical reasoning. In: Proceedings of the 2017 IEEE international conference on robotics and automation (ICRA). IEEE, pp 4270\u20134277","DOI":"10.1109\/ICRA.2017.7989491"},{"key":"5809_CR30","doi-asserted-by":"crossref","unstructured":"Pasupulety U, Adwaith CD, Hegde S et al (2018) Feature selection using fast ensemble learning for network intrusion detection. In: Proceedings of the international conference on intelligent systems design and applications. Springer, Cham, pp 967\u2013977","DOI":"10.1007\/978-3-030-16657-1_90"},{"key":"5809_CR31","doi-asserted-by":"crossref","unstructured":"Paulauskas N, Auskalnis J (2017) Analysis of data pre-processing influence on intrusion detection using NSL-KDD dataset. In: Proceedings of the 2017 open conference of electrical, electronic and information sciences (eStream). IEEE, pp 1\u20135","DOI":"10.1109\/eStream.2017.7950325"},{"key":"5809_CR32","doi-asserted-by":"crossref","unstructured":"Pham NT, Foo E, Suriadi S et al (2018) Improving performance of intrusion detection system using ensemble methods and feature selection. In: Proceedings of the Australasian computer science week multiconference on\u2014ACSW, vol 18, pp 1\u20136","DOI":"10.1145\/3167918.3167951"},{"issue":"2017","key":"5809_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2017.07.005","volume":"134","author":"MR Raman","year":"2017","unstructured":"Raman MR, Somu N, Kirthivasan K et al (2017) An efficient intrusion detection system based on hypergraph\u2014genetic algorithm for parameter optimization and feature selection in support vector machine. Knowl Based Syst 134(2017):1\u201312","journal-title":"Knowl Based Syst"},{"issue":"1","key":"5809_CR34","doi-asserted-by":"publisher","first-page":"26","DOI":"10.9781\/ijimai.2016.415","volume":"4","author":"H Ramchoun","year":"2016","unstructured":"Ramchoun H, Idrissi MAJ, Ghanou Y et al (2016) Multilayer perceptron: architecture optimization and training. IJIMAI 4(1):26\u201330","journal-title":"IJIMAI"},{"issue":"4","key":"5809_CR35","first-page":"3345","volume":"6","author":"S Rathore","year":"2015","unstructured":"Rathore S, Saxena A, Manoria M (2015) Intrusion detection system on KDDCup99 dataset: a survey. Int J Comput Sci Inf Tech 6(4):3345\u20133348","journal-title":"Int J Comput Sci Inf Tech"},{"issue":"6","key":"5809_CR36","first-page":"556","volume":"12","author":"S Rukhaiyar","year":"2018","unstructured":"Rukhaiyar S, Alam MN, Samadhiya NK (2018) A PSO-ANN hybrid model for predicting factor of safety of slope. Int J Geotech Eng 12(6):556\u2013566","journal-title":"Int J Geotech Eng"},{"key":"5809_CR37","doi-asserted-by":"crossref","unstructured":"Salman O, Abdallah S, Elhajj IH et al (2016) Identity-based authentication scheme for the Internet of Things. In: International symposium on computers and communications, 2016, pp 1109\u20131111","DOI":"10.1109\/ISCC.2016.7543884"},{"key":"5809_CR38","doi-asserted-by":"crossref","unstructured":"Salman O, Elhajj I, Kayssi A et al (2015) An architecture for the Internet of Things with decentralized data and centralized control. In: Proceedings of the 2015 IEEE\/ACS 12th international conference of computer systems and applications (AICCSA). IEEE, 1\u20138","DOI":"10.1109\/AICCSA.2015.7507265"},{"issue":"2019","key":"5809_CR39","first-page":"148","volume":"81","author":"B Selvakumar","year":"2019","unstructured":"Selvakumar B, Muneeswaran K (2019) Firefly algorithm based feature selection for network intrusion detection. Comput Secur 81(2019):148\u2013155","journal-title":"Comput Secur"},{"key":"5809_CR40","doi-asserted-by":"crossref","unstructured":"Sornsuwit P, Jaiyen S (2015) Intrusion detection model based on ensemble learning for U2R and R2L attacks. In: Proceedings of the 2015 7th international conference on information technology and electrical engineering (ICITEE). IEEE, pp 354\u2013359","DOI":"10.1109\/ICITEED.2015.7408971"},{"issue":"7","key":"5809_CR41","doi-asserted-by":"publisher","first-page":"3261","DOI":"10.1109\/TII.2018.2819674","volume":"14","author":"C Sun","year":"2018","unstructured":"Sun C, Ma M, Zhao Z et al (2018) Sparse deep stacking network for fault diagnosis of motor. IEEE Trans Ind Inf 14(7):3261\u20133270","journal-title":"IEEE Trans Ind Inf"},{"issue":"2019","key":"5809_CR42","doi-asserted-by":"publisher","first-page":"94497","DOI":"10.1109\/ACCESS.2019.2928048","volume":"7","author":"BA Tama","year":"2019","unstructured":"Tama BA, Comuzzi M, Rhee K et al (2019) TSE-IDS: a two-stage classifier ensemble for intelligent anomaly-based intrusion detection system. IEEE Access 7(2019):94497\u201394507","journal-title":"IEEE Access"},{"key":"5809_CR43","doi-asserted-by":"crossref","unstructured":"Tayyaba SK, Shah MA, Khan OA et al (2017) Software defined network (SDN) based Internet of Things (IoT): a road ahead. In: Proceedings of the international conference on future networks and distributed systems, vol 15. ACM","DOI":"10.1145\/3102304.3102319"},{"issue":"10","key":"5809_CR44","doi-asserted-by":"publisher","first-page":"3162","DOI":"10.1007\/s10489-020-01694-4","volume":"50","author":"Q Tian","year":"2020","unstructured":"Tian Q, Han D, Li K et al (2020) An intrusion detection approach based on improved deep belief network. Appl Intell 50(10):3162\u20133178","journal-title":"Appl Intell"},{"issue":"12","key":"5809_CR45","doi-asserted-by":"publisher","first-page":"2792","DOI":"10.1109\/TCYB.2014.2316552","volume":"44","author":"J Wang","year":"2014","unstructured":"Wang J, Liao J, Zhou Y et al (2014) Differential evolution enhanced with multiobjective sorting-based mutation operators. IEEE Trans Cybern 44(12):2792\u20132805","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"5809_CR46","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1504\/IJBIC.2016.074630","volume":"8","author":"H Wang","year":"2016","unstructured":"Wang H, Wang W, Sun H et al (2016) Firefly algorithm with random attraction. Int J Bio Inspir Comput 8(1):33\u201341","journal-title":"Int J Bio Inspir Comput"},{"issue":"1","key":"5809_CR47","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1080\/09540091.2020.1753175","volume":"33","author":"T Xiao","year":"2021","unstructured":"Xiao T, Han D, He J, Li K-C, de Mello RF (2021) Multi-Keyword ranked search based on mapping set matching in cloud ciphertext storage system. Conn Sci 33(1):95\u2013112","journal-title":"Conn Sci"},{"issue":"2018","key":"5809_CR48","doi-asserted-by":"publisher","first-page":"24694","DOI":"10.1109\/ACCESS.2018.2831284","volume":"6","author":"D Yin","year":"2018","unstructured":"Yin D, Zhang L, Yang K (2018) A DDoS attack detection and mitigation with software-defined Internet of Things framework. IEEE Access 6(2018):24694\u201324705","journal-title":"IEEE Access"},{"issue":"48","key":"5809_CR49","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1016\/j.procs.2015.04.201","volume":"48","author":"M Zareapoor","year":"2015","unstructured":"Zareapoor M, Shamsolmoali P (2015) Application of credit card fraud detection: based on bagging ensemble classifier. Procedia Comput Sci 48(48):679\u2013685","journal-title":"Procedia Comput Sci"},{"key":"5809_CR50","doi-asserted-by":"publisher","first-page":"12361","DOI":"10.1007\/s00500-020-04678-1","volume":"24","author":"W Zhang","year":"2020","unstructured":"Zhang W, Han D, Li K, Massetto FI (2020) Wireless sensor network intrusion detection system based on MK-ELM. Soft Comput 24:12361\u201312374","journal-title":"Soft Comput"},{"key":"5809_CR51","doi-asserted-by":"crossref","unstructured":"Zong W, Chow Y, Susilo W et al (2018) A two-stage classifier approach for network intrusion detection. In: Proceedings of the international conference on information security practice and experience, pp 329\u2013340","DOI":"10.1007\/978-3-319-99807-7_20"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-021-05809-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-021-05809-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-021-05809-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T19:04:23Z","timestamp":1670267063000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-021-05809-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,26]]},"references-count":51,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["5809"],"URL":"https:\/\/doi.org\/10.1007\/s00500-021-05809-y","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"type":"print","value":"1432-7643"},{"type":"electronic","value":"1433-7479"}],"subject":[],"published":{"date-parts":[[2021,4,26]]},"assertion":[{"value":"8 April 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}