{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T17:02:52Z","timestamp":1774630972418,"version":"3.50.1"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"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":["J Supercomput"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s11227-023-05764-5","type":"journal-article","created":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T15:02:27Z","timestamp":1699628547000},"page":"7876-7905","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["ICS-IDS: application of big data analysis in AI-based intrusion detection systems to identify cyberattacks in ICS networks"],"prefix":"10.1007","volume":"80","author":[{"given":"Bakht Sher","family":"Ali","sequence":"first","affiliation":[]},{"given":"Inam","family":"Ullah","sequence":"additional","affiliation":[]},{"given":"Tamara","family":"Al Shloul","sequence":"additional","affiliation":[]},{"given":"Izhar Ahmed","family":"Khan","sequence":"additional","affiliation":[]},{"given":"Ijaz","family":"Khan","sequence":"additional","affiliation":[]},{"given":"Yazeed Yasin","family":"Ghadi","sequence":"additional","affiliation":[]},{"given":"Akmalbek","family":"Abdusalomov","sequence":"additional","affiliation":[]},{"given":"Rashid","family":"Nasimov","sequence":"additional","affiliation":[]},{"given":"Khmaies","family":"Ouahada","sequence":"additional","affiliation":[]},{"given":"Habib","family":"Hamam","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"5764_CR1","doi-asserted-by":"crossref","unstructured":"Adepu S, Mathur A (2016) An investigation into the response of a water treatment system to cyber-attacks. In: Proceedings of 2016 IEEE 17th International Symposium on High Assurance Systems Engineering (HASE) Orlando, FL, USA, Jan 7\u20139, 2016, pp 141\u2013148","DOI":"10.1109\/HASE.2016.14"},{"key":"5764_CR2","volume-title":"Automation, production systems, and computer-integrated manufacturing","author":"MP Groover","year":"2016","unstructured":"Groover MP (2016) Automation, production systems, and computer-integrated manufacturing. Pearson, London"},{"key":"5764_CR3","doi-asserted-by":"crossref","unstructured":"Kriaa S, Bouissou M, Colin F, Halgand Y, Pietre-Cambacedes L (2014) Safety and security interactions modeling using the BDMP formalism: case study of a pipeline. In: proceedings of 2014 International Conference on Computer Safety, Reliability, and Security, Delft, The Netherlands, 22\u201325 September 2014, pp 326\u2013341","DOI":"10.1007\/978-3-319-10506-2_22"},{"key":"5764_CR4","volume-title":"Power generation, operation, and control","author":"AJ Wood","year":"2012","unstructured":"Wood AJ, Wollenberg BF (2012) Power generation, operation, and control. Wiley, Hoboken"},{"key":"5764_CR5","doi-asserted-by":"publisher","first-page":"101677","DOI":"10.1016\/j.cose.2019.101677","volume":"89","author":"D Bhamare","year":"2020","unstructured":"Bhamare D, Zolanvari M, Erbad A, Jain R, Khan K, Meskin N (2020) Cybersecurity for industrial control systems: a survey. Comput Secur 89:101677","journal-title":"Comput Secur"},{"key":"5764_CR6","unstructured":"ICS-CERT Annual Vulnerability Coordination Report, Dept. Homeland Secur. Washington, DC, USA, 2016."},{"issue":"3","key":"5764_CR7","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1109\/MSP.2011.67","volume":"9","author":"R Langner","year":"2011","unstructured":"Langner R (2011) Stuxnet: dissecting a cyberwarfare weapon. IEEE Secur Priv 9(3):49\u201351","journal-title":"IEEE Secur Priv"},{"issue":"5","key":"5764_CR8","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.1016\/j.compeleceng.2012.06.015","volume":"38","author":"B Genge","year":"2012","unstructured":"Genge B et al (2012) A cyber-physical experimentation environment for the security analysis of networked industrial control systems. Comput Electr Eng 38(5):1146\u20131161","journal-title":"Comput Electr Eng"},{"issue":"1","key":"5764_CR9","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/MCOM.2013.6400441","volume":"51","author":"M Erol-Kantarci","year":"2013","unstructured":"Erol-Kantarci M, Mouftah HT (2013) Smart grid forensic science: applications, challenges, and open issues. IEEE Commun Mag 51(1):68\u201374","journal-title":"IEEE Commun Mag"},{"key":"5764_CR10","doi-asserted-by":"crossref","unstructured":"Nazir S, Patel S, Patel D (2018) Hyper parameters selection for image classification in convolutional neural networks. In: Proceedings of 2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC). IEEE, Berkeley, CA, USA, pp 401\u2013407","DOI":"10.1109\/ICCI-CC.2018.8482081"},{"key":"5764_CR11","unstructured":"Cheung S, Dutertre B, Fong M, Lindqvist U, Skinner K, Valdes A (2007) Using model-based intrusion detection for SCADA networks. In: Proceedings of the SCADA Security Scientific Symposium, vol 46, pp 1\u201312"},{"key":"5764_CR12","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.cose.2014.09.006","volume":"48","author":"I Friedberg","year":"2015","unstructured":"Friedberg I, Skopik F, Settanni G, Fiedler R (2015) Combating advanced persistent threats: from network event correlation to incident detection. Comput Secur 48:35\u201357","journal-title":"Comput Secur"},{"key":"5764_CR13","doi-asserted-by":"crossref","unstructured":"Fovino IN, Carcano A, De Lacheze Murel T, Trombetta A, Masera M (2010) Modbus\/DNP3 state-based intrusion detection system. In: 2010 24th IEEE International Conference on Advanced Information Networking and Applications, pp 729\u2013736","DOI":"10.1109\/AINA.2010.86"},{"key":"5764_CR14","doi-asserted-by":"crossref","unstructured":"Yang Y, McLaughlin K, Littler T, Sezer S, Pranggono B, Wang HF (2013) Intrusion detection system for IEC 60870-5-104 based SCADA networks. In: Proceedings of the IEEE Power Energy Society General Meeting, pp 1\u20135","DOI":"10.1109\/PESMG.2013.6672100"},{"key":"5764_CR15","doi-asserted-by":"crossref","unstructured":"Kang B, McLaughlin K, Sezer S (2016) Towards a stateful analysis framework for smart grid network intrusion detection. In: Proceedings of the 4th International Symposium for ICS & SCADA Cyber Security Research, pp 1\u20138","DOI":"10.14236\/ewic\/ICS2016.14"},{"key":"5764_CR16","doi-asserted-by":"publisher","first-page":"89507","DOI":"10.1109\/ACCESS.2019.2925838","volume":"7","author":"IA Khan","year":"2019","unstructured":"Khan IA et al (2019) HML-IDS: a hybrid-multilevel anomaly prediction approach for intrusion detection in SCADA systems. IEEE Access 7:89507\u201389521","journal-title":"IEEE Access"},{"key":"5764_CR17","unstructured":"Morris TH, Thornton Z, Turnipseed I (2015) Industrial control system simulation and data logging for intrusion detection system research. In: Proceedings of the 7th Annual Southeastern Cyber Security Summit, pp 3\u20134"},{"key":"5764_CR18","volume-title":"Cryptography and network security: principles and practice","author":"W Stallings","year":"2017","unstructured":"Stallings W (2017) Cryptography and network security: principles and practice. Pearson, Upper Saddle River"},{"issue":"3","key":"5764_CR19","first-page":"69","volume":"4","author":"M Bijone","year":"2016","unstructured":"Bijone M (2016) A survey on secure network: intrusion detection & prevention approaches. Am J Inf Syst 4(3):69\u201388","journal-title":"Am J Inf Syst"},{"key":"5764_CR20","unstructured":"Hodo E et al (2017) Shallow and deep networks intrusion detection system: a taxonomy and survey. arXiv preprint arXiv: 1701.02145"},{"key":"5764_CR21","doi-asserted-by":"publisher","first-page":"38597","DOI":"10.1109\/ACCESS.2019.2905633","volume":"7","author":"SM Kasongo","year":"2019","unstructured":"Kasongo SM, Sun Y (2019) A deep learning method with filter based feature engineering for wireless intrusion detection system. IEEE Access 7:38597\u201338607","journal-title":"IEEE Access"},{"key":"5764_CR22","doi-asserted-by":"publisher","first-page":"33789","DOI":"10.1109\/ACCESS.2018.2841987","volume":"6","author":"I Ahmad","year":"2018","unstructured":"Ahmad I et al (2018) Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection. IEEE access 6:33789\u201333795","journal-title":"IEEE access"},{"key":"5764_CR23","doi-asserted-by":"crossref","unstructured":"Yang X, Hui Z (2015) Intrusion detection alarm filtering technology based on ant colony clustering algorithm. In: Proceedings of 2015 Sixth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA) IEEE. Guiyang, China, pp 470\u2013473","DOI":"10.1109\/ISDEA.2015.124"},{"key":"5764_CR24","unstructured":"El-halees AM (2015) Classifying multi-class imbalance data classifying multi-class imbalance data. no. September 2013"},{"key":"5764_CR25","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.aej.2023.09.023","volume":"81","author":"S Soliman","year":"2023","unstructured":"Soliman S, Oudah W, Aljuhani A (2023) Deep learning-based intrusion detection approach for securing industrial Internet of Things. Alex Eng J 81:371\u2013383","journal-title":"Alex Eng J"},{"key":"5764_CR26","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV et al (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"issue":"1","key":"5764_CR27","doi-asserted-by":"publisher","first-page":"550","DOI":"10.3390\/s23010550","volume":"23","author":"YN Rao","year":"2023","unstructured":"Rao YN, Suresh Babu K (2023) An imbalanced generative adversarial network-based approach for network intrusion detection in an imbalanced dataset. Sensors 23(1):550","journal-title":"Sensors"},{"key":"5764_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/9704672","volume":"2018","author":"JH Seo","year":"2018","unstructured":"Seo JH, Kim YH (2018) Machine-learning approach to optimize smote ratio in class imbalance dataset for intrusion detection. Comput Intell Neurosci 2018:1\u201311","journal-title":"Comput Intell Neurosci"},{"issue":"8","key":"5764_CR29","doi-asserted-by":"publisher","first-page":"3255","DOI":"10.1007\/s13369-016-2179-2","volume":"41","author":"K Jiang","year":"2016","unstructured":"Jiang K, Lu J, Xia K (2016) A novel algorithm for imbalance data classification based on genetic algorithm improved SMOTE. Arab J Sci Eng 41(8):3255\u20133266","journal-title":"Arab J Sci Eng"},{"key":"5764_CR30","doi-asserted-by":"crossref","unstructured":"Liu J, Tang Y, Zhao H, Wang X, Li F, Zhang J (2023) CPS attack detection under limited local information in cyber security: an ensemble multi-node multi-class classification approach. ACM Trans Sens Netw","DOI":"10.1145\/3585520"},{"issue":"1","key":"5764_CR31","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1111\/j.0824-7935.2004.t01-1-00228.x","volume":"20","author":"A Estabrooks","year":"2004","unstructured":"Estabrooks A, Jo T, Japkowicz N (2004) A multiple resampling method for learning from imbalanced data sets. Comput Intell 20(1):18\u201336","journal-title":"Comput Intell"},{"key":"5764_CR32","unstructured":"Wang BX, Japkowicz N (2004) Imbalanced data set learning with synthetic samples. In: Proceedings of the IRIS Machine Learning Workshop"},{"key":"5764_CR33","doi-asserted-by":"crossref","unstructured":"Han H, Wang WY, Mao BH (2005) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: International Conference on Intelligent Computing, Springer, Berlin, Heidelberg, pp 878\u2013887","DOI":"10.1007\/11538059_91"},{"key":"5764_CR34","doi-asserted-by":"crossref","unstructured":"He H, Bai Y, Garcia EA, Li S (2008) ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp 1322\u20131328. IEEE.","DOI":"10.1109\/IJCNN.2008.4633969"},{"key":"5764_CR35","doi-asserted-by":"crossref","unstructured":"Chawla NV, Lazarevic A, Hall LO, Bowyer KW (2003) SMOTEBoost: improving prediction of the minority class in boosting. In: European Conference on Principles of Data Mining and Knowledge Discovery, Springer, Berlin, Heidelberg, pp 107\u2013119","DOI":"10.1007\/978-3-540-39804-2_12"},{"issue":"1","key":"5764_CR36","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1145\/1007730.1007736","volume":"6","author":"H Guo","year":"2004","unstructured":"Guo H, Viktor HL (2004) Learning from imbalanced data sets with boosting and data generation: the databoost-im approach. ACM SIGKDD Explor Newsl 6(1):30\u201339","journal-title":"ACM SIGKDD Explor Newsl"},{"issue":"3","key":"5764_CR37","first-page":"108","volume":"41","author":"X Wang","year":"2018","unstructured":"Wang X (2018) Design of temporal sequence association rule-based intrusion detection behavior detection system for distributed network. Mod Electron Techn 41(3):108\u2013114","journal-title":"Mod Electron Techn"},{"issue":"7","key":"5764_CR38","doi-asserted-by":"publisher","first-page":"2735","DOI":"10.1007\/s10489-018-01408-x","volume":"49","author":"\u00dc \u00c7avu\u015fo\u011flu","year":"2019","unstructured":"\u00c7avu\u015fo\u011flu \u00dc (2019) A new hybrid approach for intrusion detection using machine learning methods. Appl Intell 49(7):2735\u20132761","journal-title":"Appl Intell"},{"key":"5764_CR39","first-page":"027","volume":"21","author":"Z Fuqun","year":"2015","unstructured":"Fuqun Z (2015) Detection method of LSSVM network intrusion based on hybrid kernel function. Mod Electron Tech 21:027","journal-title":"Mod Electron Tech"},{"key":"5764_CR40","doi-asserted-by":"crossref","unstructured":"Schuster F, Paul A, Rietz R, K\u00f6nig H (2015) Potentials of using one-class SVM for detecting protocol-specific anomalies in industrial networks. In: Proceedings of 2015 IEEE Symposium Series on Computational Intelligence, Cape Town, South Africa, pp 83\u201390","DOI":"10.1109\/SSCI.2015.22"},{"key":"5764_CR41","doi-asserted-by":"crossref","unstructured":"Maglaras LA, Jiang J (2014) A real time OCSVM intrusion detection module with low overhead for SCADA systems. Int J Adv Res Artif Intell (IJARAI) 3(10)","DOI":"10.14569\/IJARAI.2014.031006"},{"key":"5764_CR42","doi-asserted-by":"crossref","unstructured":"Khan IA, Pi D, Khan N, Khan ZU, Hussain Y, Nawaz A, Ali F (2021) A privacy-conserving framework based intrusion detection method for detecting and recognizing malicious behaviours in cyber-physical power networks. Appl Intell 1\u201316","DOI":"10.1007\/s10489-021-02222-8"},{"issue":"2","key":"5764_CR43","first-page":"83","volume":"11","author":"S Nazir","year":"2021","unstructured":"Nazir S, Patel S, Patel D (2021) Autoencoder based anomaly detection for scada networks. Int J Artif Intell Mach Learn (IJAIML) 11(2):83\u201399","journal-title":"Int J Artif Intell Mach Learn (IJAIML)"},{"issue":"4","key":"5764_CR44","doi-asserted-by":"publisher","first-page":"2308","DOI":"10.1109\/TII.2014.2330796","volume":"10","author":"P Nader","year":"2014","unstructured":"Nader P, Honeine P, Beauseroy P (2014) lp-norms in one-class classification for intrusion detection in SCADA systems. IEEE Trans Industr Inf 10(4):2308\u20132317","journal-title":"IEEE Trans Industr Inf"},{"key":"5764_CR45","doi-asserted-by":"crossref","unstructured":"Beaver JM, Borges-Hink RC, Buckner MA (2013) An evaluation of machine learning methods to detect malicious SCADA communications. In: Proceedings of 2013 12th International Conference on Machine Learning and Applications, Miami, FL, USA, No 2, pp 54\u201359","DOI":"10.1109\/ICMLA.2013.105"},{"key":"5764_CR46","first-page":"49","volume":"1","author":"A Mansouri","year":"2017","unstructured":"Mansouri A, Majidi B, Shamisa A (2017) Anomaly detection in industrial control systems using evolutionary-based optimization of neural networks. Commun Adv Comput Sci Appl 1:49\u201355","journal-title":"Commun Adv Comput Sci Appl"},{"key":"5764_CR47","doi-asserted-by":"crossref","unstructured":"Shirazi SN, Gouglidis A, Syeda KN, Simpson S, Mauthe A, Stephanakis IM, Hutchison D (2016) Evaluation of anomaly detection techniques for scada communication resilience. In: Proceedings of 2016 Resilience Week (RWS), Chicago, IL, USA, pp 140\u2013145","DOI":"10.1109\/RWEEK.2016.7573322"},{"issue":"9","key":"5764_CR48","doi-asserted-by":"publisher","first-page":"735","DOI":"10.3844\/jcssp.2006.735.739","volume":"2","author":"L Al Shalabi","year":"2006","unstructured":"Al Shalabi L, Shaaban Z, Kasasbeh B (2006) Data mining: a preprocessing engine. J Comput Sci 2(9):735\u2013739","journal-title":"J Comput Sci"},{"issue":"5","key":"5764_CR49","first-page":"331","volume":"8","author":"VR Patel","year":"2011","unstructured":"Patel VR, Mehta RG (2011) Impact of outlier removal and normalization approach in modified k-means clustering algorithm. Int J Comput Sci Issues (IJCSI) 8(5):331","journal-title":"Int J Comput Sci Issues (IJCSI)"},{"key":"5764_CR50","doi-asserted-by":"crossref","unstructured":"Akbani R, Kwek S, Japkowicz N (2004) Applying support vector machines to imbalanced datasets. In: Proceedings of the European Conference on Machine Learning, Springer, Berlin, Germany, pp 39\u201350","DOI":"10.1007\/978-3-540-30115-8_7"},{"issue":"1","key":"5764_CR51","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16(1):321\u2013357","journal-title":"J Artif Intell Res"},{"issue":"11","key":"5764_CR52","first-page":"769","volume":"6","author":"I Tomek","year":"1976","unstructured":"Tomek I (1976) Two modifications of CNN. IEEE Trans Syst Man Cybern 6(11):769\u2013772","journal-title":"IEEE Trans Syst Man Cybern"},{"issue":"1","key":"5764_CR53","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21\u201327","journal-title":"IEEE Trans Inf Theory"},{"issue":"2","key":"5764_CR54","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/BF02985802","volume":"27","author":"J Franklin","year":"2005","unstructured":"Franklin J (2005) The elements of statistical learning: data mining, inference and prediction. Math Intell 27(2):83\u201385","journal-title":"Math Intell"},{"issue":"1","key":"5764_CR55","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"B Leo","year":"2001","unstructured":"Leo B (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"key":"5764_CR56","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/5236.001.0001","volume-title":"Parallel distributed processing","author":"D Rumelhart","year":"1986","unstructured":"Rumelhart D, Hinton G, Williams R (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL (eds) Parallel distributed processing, vol 1. MIT Press, Cambridge"},{"key":"5764_CR57","unstructured":"Chung J et al (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555"},{"key":"5764_CR58","doi-asserted-by":"crossref","unstructured":"Wang Y (2017) A new concept using LSTM neural networks for dynamic system identification. In: Proceedings of 2017 American Control Conference (ACC). IEEE, Seattle, WA, USA, pp 5324\u20135329","DOI":"10.23919\/ACC.2017.7963782"},{"key":"5764_CR59","doi-asserted-by":"crossref","unstructured":"Feng C, Li T, Chana D (2017) Multi-level anomaly detection in industrial control systems vi package signatures and LSTM networks. In: Proceedings of the 47th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN), pp 261\u2013272","DOI":"10.1109\/DSN.2017.34"},{"issue":"3","key":"5764_CR60","first-page":"257","volume":"43","author":"A Mansouri","year":"2021","unstructured":"Mansouri A, Majidi B, Shamisa A (2021) Metaheuristic neural networks for anomaly recognition in industrial sensor networks with packet latency and jitter for smart infrastructures. Int J Comput Appl 43(3):257\u2013266","journal-title":"Int J Comput Appl"},{"key":"5764_CR61","doi-asserted-by":"crossref","unstructured":"Brand J, Balvanz J (2005) Automation is a breeze with autoit. In: Proceedings of the 33rd annual ACM SIGUCCS conference on User services, pp 12\u201315","DOI":"10.1145\/1099435.1099439"},{"key":"5764_CR62","doi-asserted-by":"crossref","unstructured":"Sokolova M, Japkowicz N, Szpakowicz S (2006) Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In: Proceedings of the Australasian Joint Conference on Artificial Intelligence, Springer, Berlin, Germany, pp 1015\u20131021","DOI":"10.1007\/11941439_114"},{"key":"5764_CR63","doi-asserted-by":"crossref","unstructured":"Demertzis K, Iliadis L, Anezakis V-D (2018) MOLESTRA: a multi-task learning approach for real-time big data analytics. In: Proceedings of the IEEE Innovations in Intelligent Systems and Applications (INISTA), pp 1\u20138","DOI":"10.1109\/INISTA.2018.8466306"},{"issue":"8","key":"5764_CR64","doi-asserted-by":"publisher","first-page":"2752","DOI":"10.1109\/TNNLS.2019.2906302","volume":"31","author":"D D\u00edaz-Vico","year":"2019","unstructured":"D\u00edaz-Vico D, Dorronsoro JR (2019) Deep least squares fisher discriminant analysis. IEEE Trans Neural Netw Learn Syst 31(8):2752\u20132763","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5764_CR65","first-page":"1","volume":"2020","author":"P Sun","year":"2020","unstructured":"Sun P, Liu P, Li Q, Liu C, Lu X, Hao R, Chen J (2020) DL-IDS: extracting features using CNN-LSTM hybrid network for intrusion detection system. Secur Commun Netw 2020:1\u201311","journal-title":"Secur Commun Netw"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05764-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-023-05764-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05764-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T20:45:56Z","timestamp":1730493956000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-023-05764-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,10]]},"references-count":65,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["5764"],"URL":"https:\/\/doi.org\/10.1007\/s11227-023-05764-5","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,10]]},"assertion":[{"value":"26 October 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 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 authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}