{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:28:23Z","timestamp":1771025303420,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"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":["Appl Intell"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s10489-024-05505-y","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T01:01:54Z","timestamp":1716512514000},"page":"6738-6759","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep learning method for efficient cloud IDS utilizing combined behavior and flow-based features"],"prefix":"10.1007","volume":"54","author":[{"given":"Geetha","family":"T V","sequence":"first","affiliation":[]},{"given":"Deepa","family":"A J","sequence":"additional","affiliation":[]},{"given":"Mary Linda","family":"M","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,24]]},"reference":[{"issue":"2","key":"5505_CR1","first-page":"565","volume":"13","author":"P Singh","year":"2021","unstructured":"Singh P, Ranga V (2021) Attack and intrusion detection in cloud computing using an ensemble learning approach. Int J Inform Technol 13(2):565\u2013571","journal-title":"Int J Inform Technol"},{"key":"5505_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2019.102507","volume":"151","author":"M Rabbani","year":"2020","unstructured":"Rabbani M, Wang YL, Khoshkangini R, Jelodar H, Zhao R, Hu P (2020) A hybrid machine learning approach for malicious behaviour detection and recognition in cloud computing. J Netw Comput Appl 151","journal-title":"J Netw Comput Appl"},{"issue":"11","key":"5505_CR3","doi-asserted-by":"publisher","first-page":"7618","DOI":"10.1109\/TII.2021.3053304","volume":"17","author":"KD Lu","year":"2021","unstructured":"Lu KD, Zeng GQ, Luo X, Weng J, Luo W, Wu Y (2021) Evolutionary deep belief network for cyber-attack detection in industrial automation and control system. IEEE Trans Industr Inf 17(11):7618\u20137627","journal-title":"IEEE Trans Industr Inf"},{"key":"5505_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102435","volume":"110","author":"S Al","year":"2021","unstructured":"Al S, Dener M (2021) STL-HDL: a new hybrid network intrusion detection system for imbalanced dataset on big data environment. Comput Secur 110","journal-title":"Comput Secur"},{"key":"5505_CR5","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/S1353-4858(20)30056-8","volume":"5","author":"A Meryem","year":"2020","unstructured":"Meryem A, Ouahidi BE (2020) Hybrid intrusion detection system using machine learning. Netw Secur 5:8\u201319","journal-title":"Netw Secur"},{"key":"5505_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2019.102031","volume":"101","author":"M Almiani","year":"2020","unstructured":"Almiani M, AbuGhazleh A, Al-Rahayfeh A, Atiewi S, Razaque A (2020) Deep recurrent neural network for IoT intrusion detection system. Simul Model Pract Theory 101","journal-title":"Simul Model Pract Theory"},{"issue":"6","key":"5505_CR7","doi-asserted-by":"publisher","first-page":"4944","DOI":"10.1109\/JIOT.2020.3034156","volume":"8","author":"SI Popoola","year":"2020","unstructured":"Popoola SI, Adebisi B, Hammoudeh M, Gui G, Gacanin H (2020) Hybrid deep learning for botnet attack detection in the internet-of-things networks. IEEE Internet Things J 8(6):4944\u20134956","journal-title":"IEEE Internet Things J"},{"key":"5505_CR8","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.ins.2016.12.007","volume":"382","author":"M Li","year":"2017","unstructured":"Li M, Wang D (2017) Insights into randomized algorithms for neural networks: practical issues and common pitfalls. Inf Sci 382:170\u2013178","journal-title":"Inf Sci"},{"issue":"10","key":"5505_CR9","doi-asserted-by":"publisher","first-page":"3466","DOI":"10.1109\/TCYB.2017.2734043","volume":"47","author":"D Wang","year":"2017","unstructured":"Wang D, Li M (2017) Stochastic configuration networks: fundamentals and algorithms. IEEE Trans Cybernetics 47(10):3466\u20133479","journal-title":"IEEE Trans Cybernetics"},{"issue":"1","key":"5505_CR10","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1109\/TCYB.2019.2925883","volume":"51","author":"M Li","year":"2021","unstructured":"Li M, Wang D (2021) 2-D stochastic configuration networks for image data analytics. IEEE Trans Cybernetics 51(1):359\u2013372","journal-title":"IEEE Trans Cybernetics"},{"key":"5505_CR11","doi-asserted-by":"crossref","unstructured":"Wang W, Du X, Shan D, Qin R, Wang N (2020) Cloud intrusion detection method based on stacked contractive auto-encoder and support vector machine. IEEE Trans Cloud Comput\u00a010(3):1634\u20131646","DOI":"10.1109\/TCC.2020.3001017"},{"key":"5505_CR12","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.future.2020.07.042","volume":"113","author":"MT Nguyen","year":"2020","unstructured":"Nguyen MT, Kim K (2020) Genetic convolutional neural network for intrusion detection systems. Future Generation Comput Syst 113:418\u2013427","journal-title":"Future Generation Comput Syst"},{"issue":"2","key":"5505_CR13","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1016\/j.gltp.2021.08.017","volume":"2","author":"TS Pooja","year":"2021","unstructured":"Pooja TS, Shrinivasacharya P (2021) Evaluating neural networks using bi-directional LSTM for network IDS (intrusion detection systems) in cyber security. Global Transitions Proc 2(2):448\u2013454","journal-title":"Global Transitions Proc"},{"key":"5505_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106997","volume":"100","author":"R Sai Sindhu Theja","year":"2021","unstructured":"Sai Sindhu Theja R, Shyam GK (2021) An efficient metaheuristic algorithm based feature selection and recurrent neural network for DoS attack detection in cloud computing environment. Appl Soft Comput 100","journal-title":"Appl Soft Comput"},{"key":"5505_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115524","volume":"185","author":"Y Imrana","year":"2021","unstructured":"Imrana Y, Xiang Y, Ali L, Abdul-Rauf Z (2021) A bidirectional LSTM deep learning approach for intrusion detection. Expert Syst Appl 185","journal-title":"Expert Syst Appl"},{"issue":"2","key":"5505_CR16","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1016\/j.ifacol.2020.12.1342","volume":"53","author":"A Rehmer","year":"2020","unstructured":"Rehmer A, Kroll A (2020) On the vanishing and exploding gradient problem in gated recurrent units. IFAC-Papers OnLine 53(2):1243\u20131248","journal-title":"IFAC-Papers OnLine"},{"key":"5505_CR17","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.neunet.2021.08.030","volume":"144","author":"F Landi","year":"2021","unstructured":"Landi F, Baraldi L, Cornia M, Cucchiara R (2021) Working memory connections for LSTM. Neural Netw 144:334\u2013341","journal-title":"Neural Netw"},{"key":"5505_CR18","unstructured":"Li M, Sonoda S, Cao F, Wang YG, Liang J (2023) How powerful are shallow neural networks with bandlimited random weights? In International Conference on Machine Learning. PMLR. pp 19960\u201319981"},{"key":"5505_CR19","doi-asserted-by":"publisher","first-page":"185489","DOI":"10.1109\/ACCESS.2020.3029307","volume":"8","author":"MD Hossain","year":"2020","unstructured":"Hossain MD, Inoue H, Ochiai H, Fall D, Kadobayashi Y (2020) LSTM-based intrusion detection system for in-vehicle can bus communications. IEEE Access 8:185489\u2013185502","journal-title":"IEEE Access"},{"issue":"3","key":"5505_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102532","volume":"58","author":"Z Ren","year":"2021","unstructured":"Ren Z, Shen Q, Diao X, Xu H (2021) A sentiment-aware deep learning approach for personality detection from text. Inf Process Manag 58(3)","journal-title":"Inf Process Manag"},{"issue":"1","key":"5505_CR21","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.icte.2020.03.002","volume":"7","author":"SM Kasongo","year":"2021","unstructured":"Kasongo SM, Sun Y (2021) A deep gated recurrent unit based model for wireless intrusion detection system. ICT Express 7(1):81\u201387","journal-title":"ICT Express"},{"issue":"2","key":"5505_CR22","doi-asserted-by":"publisher","first-page":"e4014","DOI":"10.1002\/ett.4014","volume":"32","author":"I Sumaiya Thaseen","year":"2021","unstructured":"Sumaiya Thaseen I, Saira Banu J, Lavanya K, Rukunuddin Ghalib M, Abhishek K (2021) An integrated intrusion detection system using correlation-based attribute selection and artificial neural network. Trans Emerg Telecommun Technol 32(2):e4014","journal-title":"Trans Emerg Telecommun Technol"},{"key":"5505_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102289","volume":"106","author":"J Liu","year":"2021","unstructured":"Liu J, Gao Y, Hu F (2021) A fast network intrusion detection system using adaptive synthetic oversampling and light GBM. Computers Secur 106","journal-title":"Computers Secur"},{"key":"5505_CR24","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.future.2020.08.005","volume":"115","author":"ME Basiri","year":"2021","unstructured":"Basiri ME, Nemati S, Abdar M, Cambria E, Acharya UR (2021) ABCDM: an attention-based bidirectional CNN-RNN deep model for sentiment analysis. Future Gener Comput Syst 115:279\u2013294","journal-title":"Future Gener Comput Syst"},{"key":"5505_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102177","volume":"103","author":"Z Wang","year":"2021","unstructured":"Wang Z, Liu Y, He D, Chan S (2021) Intrusion detection methods based on integrated deep learning model. Comput Secur 103","journal-title":"Comput Secur"},{"key":"5505_CR26","doi-asserted-by":"crossref","unstructured":"Samriya JK, Kumar N (2020) A novel intrusion detection system using hybrid clustering-optimization approach in cloud computing. Mater Today Proc 2(1):23\u201354","DOI":"10.1016\/j.matpr.2020.09.614"},{"key":"5505_CR27","doi-asserted-by":"publisher","first-page":"123448","DOI":"10.1109\/ACCESS.2021.3109081","volume":"9","author":"A Fatani","year":"2021","unstructured":"Fatani A, Abd Elaziz M, Dahou A, Al-Qaness MA, Lu S (2021) IoT intrusion detection system using deep learning and enhanced transient search optimization. IEEE Access 9:123448\u2013123464","journal-title":"IEEE Access"},{"key":"5505_CR28","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1016\/j.future.2020.12.009","volume":"117","author":"HA Kholidy","year":"2021","unstructured":"Kholidy HA (2021) Detecting impersonation attacks in cloud computing environments using a centric user profiling approach. Future Gener Comput Syst 117:299\u2013320","journal-title":"Future Gener Comput Syst"},{"key":"5505_CR29","first-page":"102804","volume":"58","author":"YN Kunang","year":"2021","unstructured":"Kunang YN, Nurmaini S, Stiawan D, Suprapto BY (2021) Attack classification of an intrusion detection system using deep learning and hyperparameter optimization. J Inform Secur Appl 58:102804","journal-title":"J Inform Secur Appl"},{"issue":"15","key":"5505_CR30","doi-asserted-by":"publisher","first-page":"10129","DOI":"10.1007\/s00500-021-05987-9","volume":"25","author":"Z Wang","year":"2021","unstructured":"Wang Z, Xu Z, He D, Chan S (2021) Deep logarithmic neural network for internet intrusion detection. Soft Comput 25(15):10129\u201310152","journal-title":"Soft Comput"},{"issue":"4","key":"5505_CR31","doi-asserted-by":"publisher","first-page":"3221","DOI":"10.1007\/s10586-020-03082-6","volume":"23","author":"AN Jaber","year":"2020","unstructured":"Jaber AN, Rehman SU (2020) FCM\u2013SVM based intrusion detection system for cloud computing environment. Cluster Comput 23(4):3221\u20133231","journal-title":"Cluster Comput"},{"key":"5505_CR32","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.comcom.2022.12.010","volume":"199","author":"SM Kasongo","year":"2023","unstructured":"Kasongo SM (2023) A deep learning technique for intrusion detection system using a recurrent neural networks based framework. Comput Commun 199:113\u2013125","journal-title":"Comput Commun"},{"key":"5505_CR33","first-page":"100612","volume":"25","author":"K Samunnisa","year":"2023","unstructured":"Samunnisa K, Kumar GS, Madhavi K (2023) Intrusion detection system in distributed cloud computing: hybrid clustering and classification methods. Meas: Sens 25:100612","journal-title":"Meas: Sens"},{"key":"5505_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.teler.2023.100053","volume":"10","author":"V Hnamte","year":"2023","unstructured":"Hnamte V, Hussain J (2023) DCNNBiLSTM: an efficient hybrid deep learning-based intrusion detection system. Telematics Inf Rep 10","journal-title":"Telematics Inf Rep"},{"issue":"2","key":"5505_CR35","doi-asserted-by":"publisher","DOI":"10.3390\/s21020656","volume":"21","author":"X Larriva-Novo","year":"2021","unstructured":"Larriva-Novo X, Villagr\u00e1 VA, Vega-Barbas M, Rivera D, Sanz Rodrigo M (2021) An IoT-focused intrusion detection system approach based on preprocessing characterization for cybersecurity datasets. Sensors 21(2)","journal-title":"Sensors"},{"key":"5505_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.119887","volume":"221","author":"T Peng","year":"2021","unstructured":"Peng T, Zhang C, Zhou J, Nazir MS (2021) An integrated framework of bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting. Energy 221","journal-title":"Energy"},{"key":"5505_CR37","doi-asserted-by":"crossref","unstructured":"Faris H, Mirjalili S, Aljarah I, Mafarja M, Heidari AA (2020) Salp swarm algorithm: theory, literature review, and application in extreme learning machines. Nature-inspired optimizers: theories, literature reviews and applications, pp 185\u2013199","DOI":"10.1007\/978-3-030-12127-3_11"},{"key":"5505_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114107","volume":"166","author":"D Po\u0142ap","year":"2021","unstructured":"Po\u0142ap D, Wo\u017aniak M (2021) Red fox optimization algorithm. Expert Syst Appl 166","journal-title":"Expert Syst Appl"},{"issue":"14","key":"5505_CR39","doi-asserted-by":"publisher","first-page":"9859","DOI":"10.1007\/s00521-019-04570-6","volume":"32","author":"M Shehab","year":"2020","unstructured":"Shehab M, Abualigah L, Al Hamad H, Alabool H, Alshinwan M, Khasawneh AM (2020) Moth\u2013flame optimization algorithm: variants and applications. Neural Comput Appl 32(14):9859\u20139884","journal-title":"Neural Comput Appl"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05505-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05505-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05505-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T12:24:44Z","timestamp":1718454284000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05505-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":39,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["5505"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05505-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4]]},"assertion":[{"value":"4 May 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants and\/or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"There is no informed consent for this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Authors declares that they have no conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}