{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T04:05:48Z","timestamp":1770091548806,"version":"3.49.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T00:00:00Z","timestamp":1698710400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T00:00:00Z","timestamp":1698710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mobile Netw Appl"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s11036-023-02257-w","type":"journal-article","created":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T06:01:53Z","timestamp":1698732113000},"page":"373-384","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Class Scatter Ratio Based Mahalanobis Distance Approach for Detection of Internet of Things Traffic Anomalies"],"prefix":"10.1007","volume":"29","author":[{"given":"Daegeon","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Velliangiri","family":"S.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3660-380X","authenticated-orcid":false,"given":"Bhuvaneswari Amma","family":"N.G.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongoun","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,31]]},"reference":[{"key":"2257_CR1","doi-asserted-by":"crossref","unstructured":"Khan AR, Kashif M, Jhaveri RH, Raut R, Saba T, Bahaj SA (2022) Deep learning for intrusion detection and security of internet of things (iot): current analysis, challenges, and possible solutions. Secur Commun Netw 2022","DOI":"10.1155\/2022\/4016073"},{"key":"2257_CR2","doi-asserted-by":"publisher","unstructured":"Douiba M, Benkirane S, Guezzaz A, Azrour M (2022) Anomaly detection model based on gradient boosting and decision tree for iot environments security. J Reliable Intell Environ pp 1\u201312. https:\/\/doi.org\/10.1007\/s40860-022-00184-3","DOI":"10.1007\/s40860-022-00184-3"},{"key":"2257_CR3","doi-asserted-by":"crossref","unstructured":"Gyamfi E, Jurcut A (2022) M-tads: A multi-trust dos attack detection system for mec-enabled industrial lot. In: 2022 IEEE 27th International workshop on computer aided modeling and design of communication links and networks (CAMAD), IEEE. pp 166\u2013172","DOI":"10.1109\/CAMAD55695.2022.9966900"},{"key":"2257_CR4","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1109\/TSE.1987.232894","volume":"2","author":"DE Denning","year":"1987","unstructured":"Denning DE (1987) An intrusion-detection model. IEEE Trans Softw Eng 2:222\u2013232. https:\/\/doi.org\/10.1109\/TSE.1987.232894","journal-title":"IEEE Trans Softw Eng"},{"key":"2257_CR5","doi-asserted-by":"crossref","unstructured":"Velliangiri S, NG BA, Baik N-K (2023) Detection of dos attacks in smart city networks with feature distance maps: A statistical approach. IEEE Internet Things J","DOI":"10.1109\/JIOT.2023.3264670"},{"key":"2257_CR6","doi-asserted-by":"crossref","unstructured":"She R, Fan P (2022) From mim-based gan to anomaly detection: Event probability influence on generative adversarial networks. IEEE Internet Things J","DOI":"10.1109\/JIOT.2022.3161630"},{"key":"2257_CR7","doi-asserted-by":"publisher","first-page":"138432","DOI":"10.1109\/ACCESS.2021.3118573","volume":"9","author":"T Wisanwanichthan","year":"2021","unstructured":"Wisanwanichthan T, Thammawichai M (2021) A double-layered hybrid approach for network intrusion detection system using combined naive bayes and svm. IEEE Access 9:138432\u2013138450. https:\/\/doi.org\/10.1109\/ACCESS.2021.3118573","journal-title":"IEEE Access"},{"key":"2257_CR8","first-page":"265","volume-title":"A survey on outlier detection in internet of things big data","author":"AA Al-khatib","year":"2020","unstructured":"Al-khatib AA, Mohammed B, Abdelmajid K (2020) A survey on outlier detection in internet of things big data. Big Data-Enabled Internet of Things; IET, London, UK, pp 265\u2013272"},{"issue":"2","key":"2257_CR9","doi-asserted-by":"publisher","first-page":"410","DOI":"10.3390\/s22020410","volume":"22","author":"MA Khan","year":"2022","unstructured":"Khan MA, Nasralla MM, Umar MM, Khan S, Choudhury N (2022) An efficient multilevel probabilistic model for abnormal traffic detection in wireless sensor networks. Sensors. 22(2):410","journal-title":"Sensors."},{"issue":"1","key":"2257_CR10","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1109\/COMST.2014.2336610","volume":"17","author":"DJ Weller-Fahy","year":"2015","unstructured":"Weller-Fahy DJ, Borghetti BJ, Sodemann AA (2015) A survey of distance and similarity measures used within network intrusion anomaly detection. IEEE Commun Surv & Tutorials 17(1):70\u201391. https:\/\/doi.org\/10.1109\/COMST.2014.2336610","journal-title":"IEEE Commun Surv & Tutorials"},{"key":"2257_CR11","doi-asserted-by":"publisher","first-page":"19024","DOI":"10.1109\/ACCESS.2023.3246660","volume":"11","author":"S Yaqoob","year":"2023","unstructured":"Yaqoob S, Hussain A, Subhan F, Pappalardo G, Awais M (2023) Deep learning based anomaly detection for fog-assisted iovs network. IEEE Access. 11:19024\u201319038. https:\/\/doi.org\/10.1109\/ACCESS.2023.3246660","journal-title":"IEEE Access."},{"issue":"3","key":"2257_CR12","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1109\/LWC.2021.3133479","volume":"11","author":"NAE Kuadey","year":"2022","unstructured":"Kuadey NAE, Maale GT, Kwantwi T, Sun G, Liu G (2022) Deepsecure: Detection of distributed denial of service attacks on 5g network slicing-deep learning approach. IEEE Wirel Commun Lett 11(3):488\u2013492. https:\/\/doi.org\/10.1109\/LWC.2021.3133479","journal-title":"IEEE Wirel Commun Lett"},{"key":"2257_CR13","doi-asserted-by":"crossref","unstructured":"Pascoal C, De\u00a0Oliveira MR, Valadas R, Filzmoser P, Salvador P, Pacheco A (2012) Robust feature selection and robust pca for internet traffic anomaly detection. In: 2012 Proceedings Ieee Infocom, IEEE. pp 1755\u20131763","DOI":"10.1109\/INFCOM.2012.6195548"},{"issue":"2","key":"2257_CR14","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1109\/TPDS.2013.146","volume":"25","author":"Z Tan","year":"2014","unstructured":"Tan Z, Jamdagni A, He X, Nanda P, Liu RP (2014) A system for denial-of-service attack detection based on multivariate correlation analysis. IEEE Trans Parallel Distrib Syst 25(2):447\u2013456. https:\/\/doi.org\/10.1109\/TPDS.2013.146","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"5","key":"2257_CR15","doi-asserted-by":"publisher","first-page":"71","DOI":"10.4186\/ej.2019.23.5.71","volume":"23","author":"S Jaiyen","year":"2019","unstructured":"Jaiyen S, Sornsuwit P (2019) A new incremental decision tree learning for cyber security based on ilda and mahalanobis distance. Eng J 23(5):71\u201388","journal-title":"Eng J"},{"key":"2257_CR16","doi-asserted-by":"crossref","unstructured":"Bhallavi T, Roychowdhury S, Bhosale A, Tiwari A (2021) Network intrusion detection using principal component\u2013mahalanobis taguchi system (pc-mts) approach. In: 2021 International Conference on Maintenance and Intelligent Asset Management (ICMIAM), IEEE. pp 1\u20136","DOI":"10.1109\/ICMIAM54662.2021.9715212"},{"key":"2257_CR17","doi-asserted-by":"crossref","unstructured":"Bhuvaneswari\u00a0Amma N, Valarmathi P (2022) Iotindet: Detecting internet of things intrusions with class scatter ratio and hellinger distance statistics. In: International Conference on Information Systems Security, Springer. pp 155\u2013168","DOI":"10.1007\/978-3-031-23690-7_9"},{"issue":"1","key":"2257_CR18","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/s10586-020-03120-3","volume":"24","author":"N Bhuvaneswari Amma","year":"2021","unstructured":"Bhuvaneswari Amma N, Selvakumar S (2021) A statistical class center based triangle area vector method for detection of denial of service attacks. Clust Comput 24(1):393\u2013415. https:\/\/doi.org\/10.1007\/s10586-020-03120-3","journal-title":"Clust Comput"},{"key":"2257_CR19","doi-asserted-by":"publisher","unstructured":"Gangula R (2022) Network intrusion detection system for internet of things based on enhanced flower pollination algorithm and ensemble classifier. Concurr Comput Pract Experience pp 7103. https:\/\/doi.org\/10.1002\/cpe.7103","DOI":"10.1002\/cpe.7103"},{"key":"2257_CR20","doi-asserted-by":"crossref","unstructured":"Yin Q (2022) Design and application of smart city internet of things service platform based on fuzzy clustering algorithm. Mob Inform Syst 2022","DOI":"10.1155\/2022\/8405306"},{"key":"2257_CR21","doi-asserted-by":"crossref","unstructured":"Liu Y, Gu Y, Shen X, Liao Q, Yu Q (2022) Msca: An unsupervised anomaly detection system for network security in backbone network. IEEE Trans Netw Sci Eng","DOI":"10.1109\/TNSE.2022.3206353"},{"issue":"1","key":"2257_CR22","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s12530-020-09342-5","volume":"12","author":"D P\u00e9rez","year":"2021","unstructured":"P\u00e9rez D, Alonso S, Mor\u00e1n A, Prada MA, Fuertes JJ, Dom\u00ednguez M (2021) Evaluation of feature learning for anomaly detection in network traffic. Evolving Syst 12(1):79\u201390","journal-title":"Evolving Syst"},{"key":"2257_CR23","doi-asserted-by":"publisher","unstructured":"Amma NGB, Subramanian S (2019) Feature correlation map based statistical approach for denial of service attacks detection. In: 2019 5th International conference on computing engineering and design (ICCED), IEEE. pp 1\u20136. https:\/\/doi.org\/10.1109\/ICCED46541.2019.9161080","DOI":"10.1109\/ICCED46541.2019.9161080"},{"key":"2257_CR24","doi-asserted-by":"publisher","unstructured":"Tan Z, Jamdagni A, He X, Nanda P, Liu RP (2011) Multivariate correlation analysis technique based on euclidean distance map for network traffic characterization. In: International Conference on Information and Communications Security, Springer. pp 388\u2013398. https:\/\/doi.org\/10.1007\/978-3-642-25243-3_31","DOI":"10.1007\/978-3-642-25243-3_31"},{"issue":"1","key":"2257_CR25","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1016\/j.patcog.2009.05.017","volume":"43","author":"C-F Tsai","year":"2010","unstructured":"Tsai C-F, Lin C-Y (2010) A triangle area based nearest neighbors approach to intrusion detection. Pattern recognit 43(1):222\u2013229. https:\/\/doi.org\/10.1016\/j.patcog.2009.05.017","journal-title":"Pattern recognit"},{"key":"2257_CR26","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2017.2715166","author":"N Moustafa","year":"2017","unstructured":"Moustafa N, Slay J (2017) Creech G (2017) Novel geometric area analysis technique for anomaly detection using trapezoidal area estimation on large-scale networks. IEEE Trans Big Data. https:\/\/doi.org\/10.1109\/TBDATA.2017.2715166","journal-title":"IEEE Trans Big Data"},{"issue":"10","key":"2257_CR27","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":"1\u20133","key":"2257_CR28","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s10994-014-5473-9","volume":"101","author":"F Iglesias","year":"2015","unstructured":"Iglesias F, Zseby T (2015) Analysis of network traffic features for anomaly detection. Mach Learn 101(1\u20133):59\u201384. https:\/\/doi.org\/10.1007\/s10994-014-5473-9","journal-title":"Mach Learn"},{"key":"2257_CR29","doi-asserted-by":"crossref","unstructured":"Aburakhia S, Tayeh T, Myers R, Shami A (2020) A transfer learning framework for anomaly detection using model of normality. In: 2020 11th IEEE Annual information technology, electronics and mobile communication conference (IEMCON), IEEE. pp 0055\u20130061","DOI":"10.1109\/IEMCON51383.2020.9284916"},{"issue":"16","key":"2257_CR30","doi-asserted-by":"publisher","first-page":"8294","DOI":"10.3390\/app12168294","volume":"12","author":"MA Alzahrani","year":"2022","unstructured":"Alzahrani MA, Alzahrani AM, Siddiqui MS (2022) Detecting ddos attacks in iot-based networks using matrix profile. Appl Sci 12(16):8294","journal-title":"Appl Sci"},{"key":"2257_CR31","doi-asserted-by":"publisher","first-page":"2383","DOI":"10.1007\/s11227-020-03323-w","volume":"77","author":"A Banitalebi Dehkordi","year":"2021","unstructured":"Banitalebi Dehkordi A, Soltanaghaei M, Boroujeni FZ (2021) The ddos attacks detection through machine learning and statistical methods in sdn. J Supercomput 77:2383\u20132415","journal-title":"J Supercomput"},{"key":"2257_CR32","unstructured":"Canberra U (2018) UNSW Bot-IoT Dataset. https:\/\/www.unsw.adfa.edu.au"},{"key":"2257_CR33","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1016\/j.future.2019.05.041","volume":"100","author":"N Koroniotis","year":"2019","unstructured":"Koroniotis N, Moustafa N, Sitnikova E, Turnbull B (2019) Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset. Futur Gener Comput Syst 100:779\u2013796","journal-title":"Futur Gener Comput Syst"}],"container-title":["Mobile Networks and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-023-02257-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11036-023-02257-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-023-02257-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T09:09:44Z","timestamp":1732871384000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11036-023-02257-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,31]]},"references-count":33,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["2257"],"URL":"https:\/\/doi.org\/10.1007\/s11036-023-02257-w","relation":{},"ISSN":["1383-469X","1572-8153"],"issn-type":[{"value":"1383-469X","type":"print"},{"value":"1572-8153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,31]]},"assertion":[{"value":"2 October 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 October 2023","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 author declares that there will be no conflict of interest","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}