{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T08:23:51Z","timestamp":1771835031630,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"7-8","license":[{"start":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T00:00:00Z","timestamp":1737504000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T00:00:00Z","timestamp":1737504000000},"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":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s13042-024-02505-9","type":"journal-article","created":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T08:33:20Z","timestamp":1737534800000},"page":"4189-4211","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Intrusion detection using a hybrid approach based on CatBoost and an enhanced inception V1"],"prefix":"10.1007","volume":"16","author":[{"given":"Lieqing","family":"Lin","sequence":"first","affiliation":[]},{"given":"Qi","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Jiasheng","family":"Qiu","sequence":"additional","affiliation":[]},{"given":"Zhenyu","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Yuerong","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Suxiang","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Langcheng","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,22]]},"reference":[{"issue":"11","key":"2505_CR1","first-page":"2458","volume":"4","author":"M Almansor","year":"2018","unstructured":"Almansor M, Gan K (2018) Intrusion detection systems: principles and perspectives. J Multidiscip Eng Sci Stud 4(11):2458\u20132925","journal-title":"J Multidiscip Eng Sci Stud"},{"issue":"4","key":"2505_CR2","doi-asserted-by":"publisher","first-page":"2371","DOI":"10.1007\/s13369-019-03970-z","volume":"45","author":"B Bhati","year":"2020","unstructured":"Bhati B, Rai C (2020) Analysis of support vector machine-based intrusion detection techniques. Arab J Sci Eng 45(4):2371\u20132383","journal-title":"Arab J Sci Eng"},{"issue":"1","key":"2505_CR3","doi-asserted-by":"publisher","first-page":"1249","DOI":"10.1007\/s12652-020-02167-9","volume":"12","author":"A Thakkar","year":"2021","unstructured":"Thakkar A, Lohiya R (2021) Attack classification using feature selection techniques: a comparative study. J Ambient Intell Humaniz Comput 12(1):1249\u20131266","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"2505_CR4","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1016\/j.ins.2019.10.069","volume":"513","author":"M Hassan","year":"2020","unstructured":"Hassan M, Gumaei A, Alsanad A, Alrubaian M, Fortino G (2020) A hybrid deep learning model for efficient intrusion detection in big data environment. Inf Sci 513:386\u2013396","journal-title":"Inf Sci"},{"key":"2505_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2019.102419","volume":"50","author":"MA Ferrag","year":"2020","unstructured":"Ferrag MA, Maglaras L, Moschoyiannis S, Janicke H (2020) Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study. J Inf Secur Appl 50:102419. https:\/\/doi.org\/10.1016\/j.jisa.2019.102419","journal-title":"J Inf Secur Appl"},{"issue":"11","key":"2505_CR6","doi-asserted-by":"publisher","first-page":"3337","DOI":"10.1007\/s13042-021-01323-7","volume":"12","author":"M Almiani","year":"2021","unstructured":"Almiani M, AbuGhazleh A, Jararweh Y, Razaque A (2021) DDOS detection in 5g-enabled IoT networks using deep Kalman backpropagation neural network. Int J Mach Learn Cybern 12(11):3337\u20133349. https:\/\/doi.org\/10.1007\/s13042-021-01323-7","journal-title":"Int J Mach Learn Cybern"},{"key":"2505_CR7","doi-asserted-by":"publisher","unstructured":"Khan RU, Zhang X, Alazab M, Kumar R (2019) An improved convolutional neural network model for intrusion detection in networks. In: 2019 Cybersecurity and cyberforensics conference (CCC). IEEE, pp 74\u201377. https:\/\/doi.org\/10.1109\/CCC.2019.000-6","DOI":"10.1109\/CCC.2019.000-6"},{"key":"2505_CR8","doi-asserted-by":"publisher","DOI":"10.3390\/app9020238","author":"Y Yang","year":"2019","unstructured":"Yang Y, Zheng K, Wu C, Niu X, Yang Y (2019) Building an effective intrusion detection system using the modified density peak clustering algorithm and deep belief networks. Appl Sci. https:\/\/doi.org\/10.3390\/app9020238","journal-title":"Appl Sci"},{"issue":"3","key":"2505_CR9","doi-asserted-by":"publisher","first-page":"8","DOI":"10.5815\/ijcnis.2019.03.02","volume":"11","author":"S Gurung","year":"2019","unstructured":"Gurung S, Ghose MK, Subedi A (2019) Deep learning approach on network intrusion detection system using NSL-KDD dataset. Int J Comput Netw Inf Secur 11(3):8\u201314. https:\/\/doi.org\/10.5815\/ijcnis.2019.03.02","journal-title":"Int J Comput Netw Inf Secur"},{"issue":"3","key":"2505_CR10","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1007\/s13042-022-01657-w","volume":"14","author":"C Zhang","year":"2023","unstructured":"Zhang C, Wang X, Zhang J, Li S, Zhang H, Liu C, Han P (2023) VESC: a new variational autoencoder based model for anomaly detection. Int J Mach Learn Cybern 14(3):683\u2013696. https:\/\/doi.org\/10.1007\/s13042-022-01657-w","journal-title":"Int J Mach Learn Cybern"},{"key":"2505_CR11","doi-asserted-by":"publisher","DOI":"10.3390\/math10234460","author":"C Zhang","year":"2022","unstructured":"Zhang C, Wang W, Liu L, Ren J, Wang L (2022) Three-branch random forest intrusion detection model. Mathematics. https:\/\/doi.org\/10.3390\/math10234460","journal-title":"Mathematics"},{"key":"2505_CR12","doi-asserted-by":"publisher","first-page":"107042","DOI":"10.1016\/j.comnet.2019.107042","volume":"168","author":"W Elmasry","year":"2020","unstructured":"Elmasry W, Akbulut A, Zaim A (2020) Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic. Comput Netw 168:107042","journal-title":"Comput Netw"},{"key":"2505_CR13","doi-asserted-by":"publisher","first-page":"99837","DOI":"10.1109\/ACCESS.2022.3206425","volume":"10","author":"A Halbouni","year":"2022","unstructured":"Halbouni A, Gunawan TS, Habaebi MH, Halbouni M, Kartiwi M, Ahmad R (2022) CNN-LSTM: hybrid deep neural network for network intrusion detection system. IEEE Access 10:99837\u201399849. https:\/\/doi.org\/10.1109\/ACCESS.2022.3206425","journal-title":"IEEE Access"},{"key":"2505_CR14","doi-asserted-by":"publisher","unstructured":"Sharafaldin I, Lashkari AH, Ghorbani AA (2018) Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: International conference on information systems security & privacy. https:\/\/doi.org\/10.5220\/0006639801080116","DOI":"10.5220\/0006639801080116"},{"key":"2505_CR15","doi-asserted-by":"publisher","DOI":"10.3390\/s23135941","author":"ECP Neto","year":"2023","unstructured":"Neto ECP, Dadkhah S, Ferreira R, Zohourian A, Lu R, Ghorbani AA (2023) CICIoT2023: a real-time dataset and benchmark for large-scale attacks in IoT environment. Sensors. https:\/\/doi.org\/10.3390\/s23135941","journal-title":"Sensors"},{"key":"2505_CR16","doi-asserted-by":"publisher","unstructured":"Altrad A (2023) IoTs traffics detection and analysis using machine learning for cybersecurity application. In: 2023 IEEE 5th Eurasia conference on IOT, communication and engineering (ECICE), pp 78\u201383. https:\/\/doi.org\/10.1109\/ECICE59523.2023.10383018","DOI":"10.1109\/ECICE59523.2023.10383018"},{"key":"2505_CR17","doi-asserted-by":"publisher","unstructured":"Abdulhammed R, Faezipour M, Musafer H, Abuzneid A (2019) Efficient network intrusion detection using PCA-based dimensionality reduction of features. In: 2019 International symposium on networks, computers and communications, Istanbul, Turkey. IEEE, pp 1\u20136. https:\/\/doi.org\/10.1109\/ISNCC.2019.8909140","DOI":"10.1109\/ISNCC.2019.8909140"},{"key":"2505_CR18","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. Comput Secur 106:102289. https:\/\/doi.org\/10.1016\/j.cose.2021.102289","journal-title":"Comput Secur"},{"key":"2505_CR19","doi-asserted-by":"crossref","unstructured":"Kim J, Kim H (2017) An effective intrusion detection classifier using long short-term memory with gradient descent optimization. In: 2017 international conference on platform technology and service (PlatCon). IEEE, pp 1\u20136","DOI":"10.1109\/PlatCon.2017.7883684"},{"key":"2505_CR20","doi-asserted-by":"publisher","first-page":"507","DOI":"10.3390\/electronics11030507","volume":"11","author":"S Shyla","year":"2022","unstructured":"Shyla S, Bhatnagar V, Bali V, Bali S (2022) Optimization of intrusion detection systems determined by ameliorated HNADAM-SGD algorithm. Electronics 11:507. https:\/\/doi.org\/10.3390\/electronics11030507","journal-title":"Electronics"},{"key":"2505_CR21","doi-asserted-by":"publisher","unstructured":"Abdaljabar ZH, Ucan ON, Alheeti KMA (2021) An intrusion detection system for IoT using KNN and decision-tree based classification. In: 2021 International conference of modern trends in information and communication technology industry (MTICTI), vol 14, pp 1\u20135. https:\/\/doi.org\/10.1109\/MTICTI54043.2021.00005","DOI":"10.1109\/MTICTI54043.2021.00005"},{"key":"2505_CR22","doi-asserted-by":"publisher","first-page":"5986","DOI":"10.3390\/s22165986","volume":"22","author":"AK Balyan","year":"2022","unstructured":"Balyan AK, Ahuja S, Lilhore UK, Sharma SK, Manoharan P, Algarni AD, Elmannai H, Raahemifar K (2022) A hybrid intrusion detection model using EGA-PSO and improved random forest method. Sensors 22:5986. https:\/\/doi.org\/10.3390\/s22165986","journal-title":"Sensors"},{"key":"2505_CR23","volume":"72","author":"MA Talukder","year":"2023","unstructured":"Talukder MA, Hasan KF, Islam MM, Uddin MA, Akhter A, Yousuf MA, Alharbi F, Moni MA (2023) A dependable hybrid machine learning model for network intrusion detection. J Inf Secur Appl 72:103405","journal-title":"J Inf Secur Appl"},{"key":"2505_CR24","doi-asserted-by":"publisher","first-page":"2306","DOI":"10.3390\/sym13122306","volume":"13","author":"A Aldallal","year":"2021","unstructured":"Aldallal A, Alisa F (2021) Effective intrusion detection system to secure data in cloud using machine learning. Symmetry 13:2306","journal-title":"Symmetry"},{"key":"2505_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102767","volume":"169","author":"S Gamage","year":"2020","unstructured":"Gamage S, Samarabandu J (2020) Deep learning methods in network intrusion detection: a survey and an objective comparison. J Netw Comput Appl 169:102767. https:\/\/doi.org\/10.1016\/j.jnca.2020.102767","journal-title":"J Netw Comput Appl"},{"key":"2505_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102748","volume":"118","author":"D Akgun","year":"2022","unstructured":"Akgun D, Hizal S, Cavusoglu U (2022) A new DDoS attacks intrusion detection model based on deep learning for cybersecurity. Comput Secur 118:102748. https:\/\/doi.org\/10.1016\/j.cose.2022.102748","journal-title":"Comput Secur"},{"key":"2505_CR27","doi-asserted-by":"publisher","first-page":"107764","DOI":"10.1016\/j.compeleceng.2022.107764","volume":"99","author":"E Qazi","year":"2022","unstructured":"Qazi E, Imran M, Haider N, Shoaib M, Razzak I (2022) An intelligent and efficient network intrusion detection system using deep learning. Comput Electr Eng 99:107764. https:\/\/doi.org\/10.1016\/j.compeleceng.2022.107764","journal-title":"Comput Electr Eng"},{"key":"2505_CR28","doi-asserted-by":"publisher","DOI":"10.3390\/s22218417","author":"AM Banaamah","year":"2022","unstructured":"Banaamah AM, Ahmad I (2022) Intrusion detection in IoT using deep learning. Sensors. https:\/\/doi.org\/10.3390\/s22218417","journal-title":"Sensors"},{"key":"2505_CR29","doi-asserted-by":"publisher","first-page":"6473507","DOI":"10.1155\/2022\/6473507","volume":"2022","author":"D Zhang","year":"2022","unstructured":"Zhang D, Dahou A, Abd Elaziz M, Chelloug SA, Awadallah MA, Al-Betar MA, Al-qaness MAA, Forestiero A (2022) Intrusion detection system for IoT based on deep learning and modified reptile search algorithm. Comput Intell Neurosci 2022:6473507. https:\/\/doi.org\/10.1155\/2022\/6473507","journal-title":"Comput Intell Neurosci"},{"key":"2505_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116545","volume":"194","author":"PR Kanna","year":"2022","unstructured":"Kanna PR, Santhi P (2022) Hybrid intrusion detection using MapReduce based black widow optimized convolutional long short-term memory neural networks. Expert Syst Appl 194:116545. https:\/\/doi.org\/10.1016\/j.eswa.2022.116545","journal-title":"Expert Syst Appl"},{"key":"2505_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2022.103405","volume":"72","author":"MA Talukder","year":"2023","unstructured":"Talukder MA, Hasan KF, Islam MM, Uddin MA, Akhter A, Yousuf MA, Alharbi F, Moni MA (2023) A dependable hybrid machine learning model for network intrusion detection. J Inf Secur Appl 72:103405. https:\/\/doi.org\/10.1016\/j.jisa.2022.103405","journal-title":"J Inf Secur Appl"},{"issue":"13","key":"2505_CR32","doi-asserted-by":"publisher","first-page":"19463","DOI":"10.1007\/s11042-022-14121-2","volume":"82","author":"R Yao","year":"2023","unstructured":"Yao R, Wang N, Chen P, Ma D, Sheng X (2023) A CNN-transformer hybrid approach for an intrusion detection system in advanced metering infrastructure. Multimed Tools Appl 82(13):19463\u201319486. https:\/\/doi.org\/10.1007\/s11042-022-14121-2","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"2505_CR33","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/j.jksuci.2015.12.004","volume":"29","author":"IS Thaseen","year":"2017","unstructured":"Thaseen IS, Kumar CA (2017) Intrusion detection model using fusion of chi-square feature selection and multi class SVM. J King Saud Univ Comput Inf Sci 29(4):462\u2013472","journal-title":"J King Saud Univ Comput Inf Sci"},{"issue":"1","key":"2505_CR34","doi-asserted-by":"publisher","first-page":"173","DOI":"10.3390\/electronics9010173","volume":"9","author":"A Khraisat","year":"2020","unstructured":"Khraisat A, Gondal I, Vamplew P, Kamruzzaman J, Alazab A (2020) Hybrid intrusion detection system based on the stacking ensemble of C5 decision tree classifier and one class support vector machine. Electronics 9(1):173","journal-title":"Electronics"},{"issue":"5","key":"2505_CR35","doi-asserted-by":"publisher","first-page":"834","DOI":"10.3390\/pr9050834","volume":"9","author":"MA Khan","year":"2021","unstructured":"Khan MA (2021) Hcrnnids: hybrid convolutional recurrent neural network-based network intrusion detection system. Processes 9(5):834","journal-title":"Processes"},{"key":"2505_CR36","doi-asserted-by":"publisher","first-page":"94826","DOI":"10.1109\/ACCESS.2021.3093313","volume":"9","author":"Y Tang","year":"2021","unstructured":"Tang Y, Li C (2021) An online network intrusion detection model based on improved regularized extreme learning machine. IEEE Access 9:94826\u201394844","journal-title":"IEEE Access"},{"issue":"4","key":"2505_CR37","doi-asserted-by":"publisher","first-page":"583","DOI":"10.3390\/sym11040583","volume":"11","author":"M Khan","year":"2019","unstructured":"Khan M, Karim M, Kim Y (2019) A scalable and hybrid intrusion detection system based on the convolutional-LSTM network. Symmetry 11(4):583","journal-title":"Symmetry"},{"key":"2505_CR38","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. Futur Gener Comput Syst 113:418\u2013427. https:\/\/doi.org\/10.1016\/j.future.2020.07.042","journal-title":"Futur Gener Comput Syst"},{"key":"2505_CR39","doi-asserted-by":"publisher","first-page":"29575","DOI":"10.1109\/ACCESS.2020.2972627","volume":"8","author":"T Su","year":"2020","unstructured":"Su T, Sun H, Zhu J, Wang S, Li Y (2020) BAT: deep learning methods on network intrusion detection using NSL-KDD dataset. IEEE Access 8:29575\u201329585. https:\/\/doi.org\/10.1109\/ACCESS.2020.2972627","journal-title":"IEEE Access"},{"key":"2505_CR40","unstructured":"Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A. CatBoost: unbiased boosting with categorical features. arXiv:1706.09516"},{"key":"2505_CR41","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2014) Going deeper with convolutions. arXiv:abs\/1409.4842","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"2505_CR42","unstructured":"Maas AL (2013) Rectifier nonlinearities improve neural network acoustic models. https:\/\/api.semanticscholar.org\/CorpusID:16489696"},{"key":"2505_CR43","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: deep learning with depthwise separable convolutions. arXiv:1610.02357","DOI":"10.1109\/CVPR.2017.195"},{"key":"2505_CR44","doi-asserted-by":"publisher","DOI":"10.3390\/rs11212483","author":"T Zhang","year":"2019","unstructured":"Zhang T, Zhang X, Shi J, Wei S (2019) Depthwise separable convolution neural network for high-speed SAR ship detection. Remote Sens. https:\/\/doi.org\/10.3390\/rs11212483","journal-title":"Remote Sens"},{"key":"2505_CR45","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2016) Inception-v4, Inception-ResNet, and the impact of residual connections on learning. arXiv:1602.07261","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"2505_CR46","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861"},{"key":"2505_CR47","doi-asserted-by":"publisher","unstructured":"Wang P, Chen P, Yuan Y, Liu D, Huang Z, Hou X, Cottrell G (2018) Understanding convolution for semantic segmentation. In: 2018 IEEE winter conference on applications of computer vision (WACV), pp 1451\u20131460. https:\/\/doi.org\/10.1109\/WACV.2018.00163","DOI":"10.1109\/WACV.2018.00163"},{"issue":"11","key":"2505_CR48","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324. https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc IEEE"},{"issue":"8","key":"2505_CR49","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"issue":"5786","key":"2505_CR50","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507. https:\/\/doi.org\/10.1126\/science.1127647","journal-title":"Science"},{"issue":"5","key":"2505_CR51","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1007\/s10791-009-9096-x","volume":"12","author":"IA Klampanos","year":"2009","unstructured":"Klampanos IA (2009) Manning, Christopher, Prabhakar Raghavan, Hinrich Sch\u00fctze: introduction to information retrieval. Inf Retrieval 12(5):609\u2013612. https:\/\/doi.org\/10.1007\/s10791-009-9096-x","journal-title":"Inf Retrieval"},{"key":"2505_CR52","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1117\/1.2819119","volume":"16","author":"C Bishop","year":"2006","unstructured":"Bishop C (2006) Pattern recognition and machine learning. J Electron Imaging 16:140\u2013155. https:\/\/doi.org\/10.1117\/1.2819119","journal-title":"J Electron Imaging"},{"key":"2505_CR53","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/j.ins.2016.01.033","volume":"340\u2013341","author":"X Deng","year":"2016","unstructured":"Deng X, Liu Q, Deng Y, Mahadevan S (2016) An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Inf Sci 340\u2013341:250\u2013261. https:\/\/doi.org\/10.1016\/j.ins.2016.01.033","journal-title":"Inf Sci"},{"key":"2505_CR54","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The elements of statistical learning.Data mining, inference, and prediction","author":"T Hastie","year":"2009","unstructured":"Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning.Data mining, inference, and prediction. Springer. https:\/\/doi.org\/10.1007\/978-0-387-84858-7"},{"key":"2505_CR55","doi-asserted-by":"publisher","first-page":"24428","DOI":"10.1109\/ACCESS.2024.3364400","volume":"12","author":"O Alghushairy","year":"2024","unstructured":"Alghushairy O, Alsini R, Alhassan Z, Alshdadi AA, Banjar A, Yafoz A, Ma X (2024) An efficient support vector machine algorithm based network outlier detection system. IEEE Access 12:24428\u201324441. https:\/\/doi.org\/10.1109\/ACCESS.2024.3364400","journal-title":"IEEE Access"},{"key":"2505_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103171","volume":"128","author":"X Han","year":"2023","unstructured":"Han X, Cui S, Liu S, Zhang C, Jiang B, Lu Z (2023) Network intrusion detection based on n-gram frequency and time-aware transformer. Comput Secur 128:103171. https:\/\/doi.org\/10.1016\/j.cose.2023.103171","journal-title":"Comput Secur"},{"key":"2505_CR57","doi-asserted-by":"publisher","unstructured":"Yin Y, Jang-Jaccard J, Sabrina F, Kwak J (2023) Improving multilayer-perceptron (MLP)-based network anomaly detection with birch clustering on CICIDS-2017 dataset. In: 2023 26th international conference on computer supported cooperative work in design (CSCWD), pp 423\u2013431. https:\/\/doi.org\/10.1109\/CSCWD57460.2023.10152640","DOI":"10.1109\/CSCWD57460.2023.10152640"},{"key":"2505_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102600","volume":"114","author":"A Chen","year":"2022","unstructured":"Chen A, Fu Y, Zheng X, Lu G (2022) An efficient network behavior anomaly detection using a hybrid DBN-LSTM network. Comput Secur 114:102600. https:\/\/doi.org\/10.1016\/j.cose.2021.102600","journal-title":"Comput Secur"},{"key":"2505_CR59","doi-asserted-by":"publisher","first-page":"19463","DOI":"10.1007\/s11042-022-14121-2","volume":"82","author":"R Yao","year":"2023","unstructured":"Yao R, Wang N, Chen P (2023) A CNN-transformer hybrid approach for an intrusion detection system in advanced metering infrastructure. Multimed Tools Appl 82:19463\u201319486. https:\/\/doi.org\/10.1007\/s11042-022-14121-2","journal-title":"Multimed Tools Appl"},{"key":"2505_CR60","doi-asserted-by":"publisher","DOI":"10.3390\/math12070948","author":"H Tan","year":"2024","unstructured":"Tan H, Wang L, Zhu D, Deng J (2024) Intrusion detection based on adaptive sample distribution dual-experience replay reinforcement learning. Mathematics. https:\/\/doi.org\/10.3390\/math12070948","journal-title":"Mathematics"},{"key":"2505_CR61","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-024-05993-2","author":"S Abbas","year":"2024","unstructured":"Abbas S, Alsubai S, Ojo S (2024) An efficient deep recurrent neural network for detection of cyberattacks in realistic IoT environment. J Supercomput. https:\/\/doi.org\/10.1007\/s11227-024-05993-2","journal-title":"J Supercomput"},{"key":"2505_CR62","doi-asserted-by":"publisher","first-page":"131661","DOI":"10.1109\/ACCESS.2023.3336678","volume":"11","author":"T-T-H Le","year":"2023","unstructured":"Le T-T-H, Wardhani RW, Putranto DSC, Jo U, Kim H (2023) Toward enhanced attack detection and explanation in intrusion detection system-based IoT environment data. IEEE Access 11:131661\u2013131676. https:\/\/doi.org\/10.1109\/ACCESS.2023.3336678","journal-title":"IEEE Access"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02505-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02505-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02505-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T03:46:00Z","timestamp":1757130360000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02505-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,22]]},"references-count":62,"journal-issue":{"issue":"7-8","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["2505"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02505-9","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,22]]},"assertion":[{"value":"6 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2025","order":3,"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 no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}