{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T01:19:46Z","timestamp":1770340786490,"version":"3.49.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"25","license":[{"start":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T00:00:00Z","timestamp":1705708800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T00:00:00Z","timestamp":1705708800000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-18162-7","type":"journal-article","created":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T04:39:27Z","timestamp":1705725567000},"page":"65949-65966","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Leveraging feature subset selection with deer hunting optimizer based deep learning for anomaly detection in secure cloud environment"],"prefix":"10.1007","volume":"83","author":[{"given":"V. Sujatha","family":"Bai","sequence":"first","affiliation":[]},{"given":"M.","family":"Punithavalli","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,20]]},"reference":[{"key":"18162_CR1","doi-asserted-by":"crossref","unstructured":"He Z, Chen P, Li X, Wang Y, Yu G, Chen C, Li X, Zheng Z (2020) A spatiotemporal deep learning approach for unsupervised anomaly detection in cloud systems. IEEE Transactions on Neural Networks and Learning Systems 34(4):1705\u20131719","DOI":"10.1109\/TNNLS.2020.3027736"},{"key":"18162_CR2","doi-asserted-by":"crossref","unstructured":"Nedelkoski S, Cardoso J, Kao O (2019) Anomaly detection from system tracing data using multimodal deep learning. In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD). IEEE, Milan, pp 179\u2013186","DOI":"10.1109\/CLOUD.2019.00038"},{"key":"18162_CR3","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.future.2020.07.020","volume":"113","author":"BA NG","year":"2020","unstructured":"NG BA, Selvakumar S (2020) Anomaly detection framework for Internet of things traffic using vector convolutional deep learning approach in fog environment. Future Gener Comput Syst 113:255\u2013265","journal-title":"Future Gener Comput Syst"},{"key":"18162_CR4","doi-asserted-by":"publisher","first-page":"107647","DOI":"10.1016\/j.comnet.2020.107647","volume":"184","author":"KN Qureshi","year":"2021","unstructured":"Qureshi KN, Jeon G, Piccialli F (2021) Anomaly detection and trust authority in artificial intelligence and cloud computing. Comput Netw 184:107647","journal-title":"Comput Netw"},{"key":"18162_CR5","doi-asserted-by":"publisher","unstructured":"Nedelkoski S, Cardoso J, Kao O (2019) Anomaly detection and classification using distributed tracing and deep learning. In: 2019 19th IEEE\/ACM international symposium on cluster, cloud and grid computing (CCGRID). IEEE, Larnaca, pp 241\u2013250. https:\/\/doi.org\/10.1109\/CCGRID.2019.00038","DOI":"10.1109\/CCGRID.2019.00038"},{"key":"18162_CR6","doi-asserted-by":"publisher","unstructured":"Li X, Chen P, Jing L, He Z, Yu G (2020) Swisslog: Robust and unified deep learning based log anomaly detection for diverse faults. In: 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE). IEEE, Coimbra, pp 92\u2013103. https:\/\/doi.org\/10.1109\/ISSRE5003.2020.00018","DOI":"10.1109\/ISSRE5003.2020.00018"},{"issue":"16","key":"18162_CR7","doi-asserted-by":"publisher","first-page":"1876","DOI":"10.3390\/electronics10161876","volume":"10","author":"I Apostol","year":"2021","unstructured":"Apostol I, Preda M, Nila C, Bica I (2021) IoT botnet anomaly detection using unsupervised deep learning. Electronics 10(16):1876","journal-title":"Electronics"},{"issue":"11","key":"18162_CR8","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.3390\/s19112451","volume":"19","author":"M Munir","year":"2019","unstructured":"Munir M, Siddiqui SA, Chattha MA, Dengel A, Ahmed S (2019) FuseAD: unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11):2451","journal-title":"Sensors"},{"key":"18162_CR9","doi-asserted-by":"publisher","first-page":"17361","DOI":"10.1007\/s00521-020-05189-8","volume":"32","author":"K Demertzis","year":"2020","unstructured":"Demertzis K, Iliadis L, Tziritas N, Kikiras P (2020) Anomaly detection via blockchained deep learning smart contracts in industry 4.0. Neural Comput Appl 32:17361\u201317378","journal-title":"Neural Comput Appl"},{"issue":"7","key":"18162_CR10","doi-asserted-by":"publisher","first-page":"e4121","DOI":"10.1002\/ett.4121","volume":"32","author":"DK Reddy","year":"2021","unstructured":"Reddy DK, Behera HS, Nayak J, Vijayakumar P, Naik B, Singh PK (2021) Deep neural network based anomaly detection in Internet of Things network traffic tracking for the applications of future smart cities. Trans Emerg Telecommun Technol 32(7):e4121","journal-title":"Trans Emerg Telecommun Technol"},{"key":"18162_CR11","first-page":"1","volume":"2020","author":"J Gao","year":"2020","unstructured":"Gao J, Liu J, Guo S, Zhang Q, Wang X (2020) A data mining method using deep learning for anomaly detection in cloud computing environment. Math Probl Eng 2020:1\u201311","journal-title":"Math Probl Eng"},{"key":"18162_CR12","first-page":"100156","volume":"6","author":"JO Onah","year":"2021","unstructured":"Onah JO, Abdullahi M, Hassan IH, Al-Ghusham A (2021) Genetic Algorithm based feature selection and Na\u00efve Bayes for anomaly detection in fog computing environment. Mach Learn Appl 6:100156","journal-title":"Mach Learn Appl"},{"issue":"1","key":"18162_CR13","doi-asserted-by":"publisher","first-page":"46","DOI":"10.36548\/jtcsst.2020.1.005","volume":"2","author":"S Shakya","year":"2020","unstructured":"Shakya S, Pulchowk LN, Smys S (2020) Anomalies detection in fog computing architectures using deep learning. J Trends Comput Sci Smart Technol 2(1):46\u201355","journal-title":"J Trends Comput Sci Smart Technol"},{"key":"18162_CR14","doi-asserted-by":"publisher","first-page":"106997","DOI":"10.1016\/j.asoc.2020.106997","volume":"100","author":"R SaiSindhuTheja","year":"2021","unstructured":"SaiSindhuTheja R, Shyam G (2021) An efficient metaheuristic algorithm based feature selection and recurrent neural network for DoS attack detection in cloud computing environment. Appl Soft Comput 100:106997","journal-title":"Appl Soft Comput"},{"key":"18162_CR15","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1016\/j.future.2021.10.033","volume":"129","author":"W Ullah","year":"2022","unstructured":"Ullah W, Ullah A, Hussain T, Muhammad K, Heidari AA, Del Ser J, Baik SW, De Albuquerque VHC (2022) Artificial Intelligence of Things-assisted two-stream neural network for anomaly detection in surveillance Big Video Data. Futur Gener Comput Syst 129:286\u2013297","journal-title":"Futur Gener Comput Syst"},{"key":"18162_CR16","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.jpdc.2019.09.013","volume":"135","author":"S Garg","year":"2020","unstructured":"Garg S, Kaur K, Batra S, Aujla GS, Morgan G, Kumar N, Zomaya AY, Ranjan R (2020) En-ABC: An ensemble artificial bee colony based anomaly detection scheme for cloud environment. J Parallel Dist Comput 135:219\u2013233","journal-title":"J Parallel Dist Comput"},{"key":"18162_CR17","first-page":"120","volume":"2019","author":"A Samir","year":"2019","unstructured":"Samir A, Pahl C (2019) Anomaly detection and analysis for clustered cloud computing reliability. Cloud Comput 2019:120","journal-title":"Cloud Comput"},{"issue":"15","key":"18162_CR18","doi-asserted-by":"publisher","first-page":"7050","DOI":"10.3390\/app11157050","volume":"11","author":"Z Ahmad","year":"2021","unstructured":"Ahmad Z, Shahid Khan A, Nisar K, Haider I, Hassan R, Haque MR, Tarmizi S, Rodrigues JJ (2021) Anomaly detection using deep neural network for IoT architecture. Appl Sci 11(15):7050","journal-title":"Appl Sci"},{"issue":"2","key":"18162_CR19","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1007\/s10586-019-02998-y","volume":"23","author":"A Alnafessah","year":"2020","unstructured":"Alnafessah A, Casale G (2020) Artificial neural networks based techniques for anomaly detection in Apache Spark. Clust Comput 23(2):1345\u20131360","journal-title":"Clust Comput"},{"key":"18162_CR20","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.knosys.2017.12.037","volume":"145","author":"M Mafarja","year":"2018","unstructured":"Mafarja M, Aljarah I, Heidari AA, Hammouri AI, Faris H, Ala\u2019M AZ, Mirjalili S (2018) Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowl-Based Syst 145:25\u201345","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"18162_CR21","first-page":"2","volume":"1","author":"W Wang","year":"2022","unstructured":"Wang W, Lei Y, Yan T, Li N, Nandi A (2022) Residual convolution long short-term memory network for machines remaining useful life prediction and uncertainty quantification. J Dyn, Monit Diagn 1(1):2\u20138","journal-title":"J Dyn, Monit Diagn"},{"issue":"24","key":"18162_CR22","doi-asserted-by":"publisher","first-page":"12019","DOI":"10.3390\/app112412019","volume":"11","author":"CC Chuang","year":"2021","unstructured":"Chuang CC, Lee CC, Yeng CH, So EC, Chen YJ (2021) Attention mechanism-based convolutional long short-term memory neural networks to electrocardiogram-based blood pressure estimation. Appl Sci 11(24):12019","journal-title":"Appl Sci"},{"key":"18162_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/5396327","volume":"2021","author":"W Ha","year":"2021","unstructured":"Ha W, Vahedi Z (2021) Automatic breast tumor diagnosis in mri based on a hybrid cnn and feature-based method using improved deer hunting optimization algorithm. Comput Intell Neurosci 2021:1\u201311","journal-title":"Comput Intell Neurosci"},{"key":"18162_CR24","doi-asserted-by":"publisher","first-page":"11283","DOI":"10.3390\/app112311283","volume":"11","author":"HC Lin","year":"2021","unstructured":"Lin HC, Wang P, Chao KM, Lin WH, Yang ZY (2021) Ensemble Learning for Threat Classification in Network Intrusion Detection on a Security Monitoring System for Renewable Energy. Appl Sci 11:11283. https:\/\/doi.org\/10.3390\/app112311283","journal-title":"Appl Sci"},{"key":"18162_CR25","doi-asserted-by":"publisher","first-page":"7409","DOI":"10.3390\/s22197409","volume":"22","author":"D OgobuchiOkey","year":"2022","unstructured":"OgobuchiOkey D, Sarah Maidin S, Adasme P, Lopes Rosa R, Saadi M, Carrillo Melgarejo D, Zegarra Rodr\u00edguez D (2022) BoostedEnML Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning. Sensors 22:7409. https:\/\/doi.org\/10.3390\/s22197409","journal-title":"Sensors"},{"key":"18162_CR26","doi-asserted-by":"publisher","first-page":"2276","DOI":"10.3390\/app13042276","volume":"13","author":"S Alzughaibi","year":"2023","unstructured":"Alzughaibi S, El khediri S (2023) A Cloud Intrusion Detection Systems Based on DNN Using Backpropagation and PSO on the CSE-CIC-IDS2018 Dataset. Appl. Sci. 13:2276. https:\/\/doi.org\/10.3390\/app13042276","journal-title":"Appl. Sci."},{"key":"18162_CR27","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.3390\/electronics12061423","volume":"12","author":"B RM","year":"2023","unstructured":"RM B, MK JK (2023) Intrusion Detection on AWS Cloud through Hybrid Deep Learning Algorithm. Electronics 12:1423. https:\/\/doi.org\/10.3390\/electronics12061423","journal-title":"Electronics"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18162-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18162-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18162-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T10:12:13Z","timestamp":1720519933000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18162-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,20]]},"references-count":27,"journal-issue":{"issue":"25","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["18162"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18162-7","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,20]]},"assertion":[{"value":"4 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human participants and\/or animals"}},{"value":"The authors have expressed no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}