{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T14:33:47Z","timestamp":1780756427308,"version":"3.54.1"},"reference-count":71,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T00:00:00Z","timestamp":1686268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fondo Europeo di Sviluppo Regionale Puglia Programma Operativo Regionale (POR) Puglia 2014-2020-Axis I-Specific Objective 1a-Action 1.1 (Research and Development) Project Titled: CyberSecurity and Security Operation Center (SOC) Product Suite by BV TECH S.p.A.","award":["CUP\/CIG B93G18000040007"],"award-info":[{"award-number":["CUP\/CIG B93G18000040007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Web phishing is a form of cybercrime aimed at tricking people into visiting malicious URLs to exfiltrate sensitive data. Since the structure of a malicious URL evolves over time, phishing detection mechanisms that can adapt to such variations are paramount. Furthermore, web phishing detection is an unbalanced classification task, as legitimate URLs outnumber malicious ones in real-life cases. Deep learning (DL) has emerged as a promising technique to minimize concept drift to enhance web phishing detection. Deep reinforcement learning (DRL) combines DL with reinforcement learning (RL); that is, a sequential decision-making paradigm in which the problem to be addressed is expressed as a Markov decision process (MDP). Recent studies have proposed an ad hoc MDP formulation to tackle unbalanced classification tasks called the imbalanced classification Markov decision process (ICMDP). In this paper, we exploit the ICMDP to present a double deep Q-Network (DDQN)-based classifier to address the unbalanced web phishing classification problem. The proposed algorithm is evaluated on a Mendeley web phishing dataset, from which three different data imbalance scenarios are generated. Despite a significant training time, it results in better geometric mean, index of balanced accuracy, F1 score, and area under the ROC curve than other DL-based classifiers combined with data-level sampling techniques in all test cases.<\/jats:p>","DOI":"10.3390\/computers12060118","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T08:37:33Z","timestamp":1686299853000},"page":"118","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Unbalanced Web Phishing Classification through Deep Reinforcement Learning"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6526-554X","authenticated-orcid":false,"given":"Antonio","family":"Maci","sequence":"first","affiliation":[{"name":"Cybersecurity Laboratory, BV TECH S.p.A., 20123 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1094-8199","authenticated-orcid":false,"given":"Alessandro","family":"Santorsola","sequence":"additional","affiliation":[{"name":"Cybersecurity Laboratory, BV TECH S.p.A., 20123 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7263-4999","authenticated-orcid":false,"given":"Antonio","family":"Coscia","sequence":"additional","affiliation":[{"name":"Cybersecurity Laboratory, BV TECH S.p.A., 20123 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7344-636X","authenticated-orcid":false,"given":"Andrea","family":"Iannacone","sequence":"additional","affiliation":[{"name":"Cybersecurity Laboratory, BV TECH S.p.A., 20123 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,9]]},"reference":[{"key":"ref_1","first-page":"2346","article-title":"Learning under Concept Drift: A Review","volume":"31","author":"Lu","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Thampi, S.M., Piramuthu, S., Li, K.C., Berretti, S., Wozniak, M., and Singh, D. (2020, January 14\u201317). Concept Drift Detection in Phishing Using Autoencoders. Proceedings of the Machine Learning and Metaheuristics Algorithms, and Applications (SoMMA), Chennai, India.","DOI":"10.1007\/978-981-16-0419-5"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Raza, M., Jayasinghe, N.D., and Muslam, M.M.A. (2021, January 13\u201316). A Comprehensive Review on Email Spam Classification using Machine Learning Algorithms. Proceedings of the 2021 International Conference on Information Networking (ICOIN), Jeju, Republic of Korea.","DOI":"10.1109\/ICOIN50884.2021.9334020"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MSP.2017.2743240","article-title":"Deep Reinforcement Learning: A Brief Survey","volume":"34","author":"Arulkumaran","year":"2017","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, X., Wang, S., Liang, X., Zhao, D., Huang, J., Xu, X., Dai, B., and Miao, Q. (2022). Deep Reinforcement Learning: A Survey. IEEE Trans. Neural Netw. Learn. Syst., in press.","DOI":"10.1109\/TNNLS.2022.3207346"},{"key":"ref_6","unstructured":"Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013). Playing Atari with Deep Reinforcement Learning. arXiv."},{"key":"ref_7","unstructured":"Stekolshchik, R. (2022). Some approaches used to overcome overestimation in Deep Reinforcement Learning algorithms. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"van Hasselt, H., Guez, A., and Silver, D. (2015). Deep Reinforcement Learning with Double Q-learning. arXiv.","DOI":"10.1609\/aaai.v30i1.10295"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"112963","DOI":"10.1016\/j.eswa.2019.112963","article-title":"Application of deep reinforcement learning to intrusion detection for supervised problems","volume":"141","author":"Carro","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Nguyen, T.T., and Reddi, V.J. (2021). Deep Reinforcement Learning for Cyber Security. IEEE Trans. Neural Netw. Learn. Syst., in press.","DOI":"10.1109\/TNNLS.2021.3121870"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s42979-021-00535-6","article-title":"Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"36429","DOI":"10.1109\/ACCESS.2022.3151903","article-title":"Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions","volume":"10","author":"Do","year":"2022","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chatterjee, M., and Namin, A.S. (2019, January 15\u201319). Detecting Phishing Websites through Deep Reinforcement Learning. Proceedings of the 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), Milwaukee, WI, USA.","DOI":"10.1109\/COMPSAC.2019.10211"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Do, N.Q., Selamat, A., Krejcar, O., Yokoi, T., and Fujita, H. (2021). Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study. Appl. Sci., 11.","DOI":"10.3390\/app11199210"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.ins.2019.11.004","article-title":"Data imbalance in classification: Experimental evaluation","volume":"513","author":"Thabtah","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dablain, D., Krawczyk, B., and Chawla, N.V. (2022). DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data. IEEE Trans. Neural Netw. Learn. Syst., in press.","DOI":"10.1109\/TNNLS.2021.3136503"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s40537-019-0192-5","article-title":"Survey on deep learning with class imbalance","volume":"6","author":"Johnson","year":"2019","journal-title":"J. Big Data"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2585","DOI":"10.1007\/s10115-021-01605-0","article-title":"Model complexity of deep learning: A survey","volume":"63","author":"Hu","year":"2021","journal-title":"Knowl. Inf. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Siddhesh Vijay, J., Kulkarni, K., and Arya, A. (2022, January 27\u201329). Metaheuristic Optimization of Neural Networks for Phishing Detection. Proceedings of the 2022 3rd International Conference for Emerging Technology (INCET), Belgaum, India.","DOI":"10.1109\/INCET54531.2022.9824203"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"501","DOI":"10.3390\/digital2040027","article-title":"Significance of machine learning for detection of malicious websites on an unbalanced dataset","volume":"2","author":"Ali","year":"2022","journal-title":"Digital"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Pristyanto, Y., and Dahlan, A. (2019, January 20\u201321). Hybrid Resampling for Imbalanced Class Handling on Web Phishing Classification Dataset. Proceedings of the 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia.","DOI":"10.1109\/ICITISEE48480.2019.9003803"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2488","DOI":"10.1007\/s10489-020-01637-z","article-title":"Deep Reinforcement Learning for Imbalanced Classification","volume":"50","author":"Lin","year":"2020","journal-title":"Appl. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"133653","DOI":"10.1109\/ACCESS.2019.2941229","article-title":"Q-Learning Algorithms: A Comprehensive Classification and Applications","volume":"7","author":"Jang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_24","unstructured":"Lafferty, J., Williams, C., Shawe-Taylor, J., Zemel, R., and Culotta, A. (2010, January 6\u201311). Double Q-learning. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1109\/COMST.2018.2847722","article-title":"A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection","volume":"21","author":"Mishra","year":"2019","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"ref_26","first-page":"589","article-title":"Deep Reinforcement Learning in the Advanced Cybersecurity Threat Detection and Protection","volume":"25","author":"Sewak","year":"2022","journal-title":"Inf. Syst. Front."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, Y., Dong, M., Ota, K., Li, J., and Wu, J. (2018, January 17\u201319). Deep Reinforcement Learning based Smart Mitigation of DDoS Flooding in Software-Defined Networks. Proceedings of the 2018 IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Barcelona, Spain.","DOI":"10.1109\/CAMAD.2018.8514971"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shi, G., and He, G. (2021, January 17\u201319). Collaborative Multi-agent Reinforcement Learning for Intrusion Detection. Proceedings of the 2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC), Beijing, China.","DOI":"10.1109\/IC-NIDC54101.2021.9660402"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4197","DOI":"10.1109\/TNSM.2021.3120804","article-title":"Network Abnormal Traffic Detection Model Based on Semi-Supervised Deep Reinforcement Learning","volume":"18","author":"Dong","year":"2021","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_30","first-page":"834048","article-title":"A Study on the Efficacy of Deep Reinforcement Learning for Intrusion Detection","volume":"4","author":"Angin","year":"2020","journal-title":"Sak. Univ. J. Comput. Inf. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hsu, Y.F., and Matsuoka, M. (2020, January 9\u201311). A Deep Reinforcement Learning Approach for Anomaly Network Intrusion Detection System. Proceedings of the 2020 IEEE 9th International Conference on Cloud Networking (CloudNet), Virtual.","DOI":"10.1109\/CloudNet51028.2020.9335796"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sujatha, V., Prasanna, K.L., Niharika, K., Charishma, V., and Sai, K.B. (2023, January 23\u201325). Network Intrusion Detection using Deep Reinforcement Learning. Proceedings of the 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India.","DOI":"10.1109\/ICCMC56507.2023.10083673"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.comnet.2019.05.013","article-title":"Adversarial environment reinforcement learning algorithm for intrusion detection","volume":"159","author":"Caminero","year":"2019","journal-title":"Comput. Netw."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yang, B., Arshad, M.H., and Zhao, Q. (2022). Packet-Level and Flow-Level Network Intrusion Detection Based on Reinforcement Learning and Adversarial Training. Algorithms, 15.","DOI":"10.3390\/a15120453"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Alavizadeh, H., Alavizadeh, H., and Jang-Jaccard, J. (2022). Deep Q-Learning Based Reinforcement Learning Approach for Network Intrusion Detection. Computers, 11.","DOI":"10.3390\/computers11030041"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wheelus, C., Bou-Harb, E., and Zhu, X. (2018, January 6\u20139). Tackling Class Imbalance in Cyber Security Datasets. Proceedings of the 2018 IEEE International Conference on Information Reuse and Integration (IRI), Salt Lake City, UT, USA.","DOI":"10.1109\/IRI.2018.00041"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"10611","DOI":"10.1007\/s11227-023-05073-x","article-title":"Addressing the class imbalance problem in network intrusion detection systems using data resampling and deep learning","volume":"79","author":"Abdelkhalek","year":"2023","journal-title":"J. Supercomput."},{"key":"ref_38","first-page":"243","article-title":"Analyzing the impact of unbalanced data on web spam classification","volume":"Volume 373","author":"Laza","year":"2015","journal-title":"Proceedings of the Distributed Computing and Artificial Intelligence, 12th International Conference"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Livara, A., and Hernandez, R. (2022, January 21\u201322). An Empirical Analysis of Machine Learning Techniques in Phishing E-mail detection. Proceedings of the 2022 International Conference for Advancement in Technology (ICONAT), Goa, India.","DOI":"10.1109\/ICONAT53423.2022.9725434"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/TDSC.2018.2864993","article-title":"Learning from the Ones that Got Away: Detecting New Forms of Phishing Attacks","volume":"15","author":"Gutierrez","year":"2018","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ahsan, M., Gomes, R., and Denton, A. (2018, January 3\u20135). SMOTE Implementation on Phishing Data to Enhance Cybersecurity. Proceedings of the 2018 IEEE International Conference on Electro\/Information Technology (EIT), Rochester, MI, USA.","DOI":"10.1109\/EIT.2018.8500086"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Priya, S., and Uthra, R.A. (2021). Deep learning framework for handling concept drift and class imbalanced complex decision-making on streaming data. Complex Intell. Syst., in press.","DOI":"10.1007\/s40747-021-00456-0"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Abdul Samad, S.R., Balasubaramanian, S., Al-Kaabi, A.S., Sharma, B., Chowdhury, S., Mehbodniya, A., Webber, J.L., and Bostani, A. (2023). Analysis of the Performance Impact of Fine-Tuned Machine Learning Model for Phishing URL Detection. Electronics, 12.","DOI":"10.3390\/electronics12071642"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"93089","DOI":"10.1109\/ACCESS.2021.3093094","article-title":"An Effective Cost-Sensitive XGBoost Method for Malicious URLs Detection in Imbalanced Dataset","volume":"9","author":"He","year":"2021","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Tan, G., Zhang, P., Liu, Q., Liu, X., Zhu, C., and Dou, F. (2018, January 1\u20133). Adaptive Malicious URL Detection: Learning in the Presence of Concept Drifts. Proceedings of the 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications\/12th IEEE International Conference on Big Data Science and Engineering (TrustCom\/BigDataSE), New York, NY, USA.","DOI":"10.1109\/TrustCom\/BigDataSE.2018.00107"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhao, C., Xin, Y., Li, X., Yang, Y., and Chen, Y. (2020). A Heterogeneous Ensemble Learning Framework for Spam Detection in Social Networks with Imbalanced Data. Appl. Sci., 10.","DOI":"10.3390\/app10030936"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Bu, S.J., and Cho, S.B. (2021). Deep Character-Level Anomaly Detection Based on a Convolutional Autoencoder for Zero-Day Phishing URL Detection. Electronics, 10.","DOI":"10.3390\/electronics10121492"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"102372","DOI":"10.1016\/j.cose.2021.102372","article-title":"Phishing websites detection via CNN and multi-head self-attention on imbalanced datasets","volume":"108","author":"Xiao","year":"2021","journal-title":"Comput. Secur."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Anand, A., Gorde, K., Antony Moniz, J.R., Park, N., Chakraborty, T., and Chu, B.T. (2018, January 10\u201313). Phishing URL Detection with Oversampling based on Text Generative Adversarial Networks. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8622547"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Naim, O., Cohen, D., and Ben-Gal, I. (2023). Malicious website identification using design attribute learning. Int. J. Inf. Secur., in press.","DOI":"10.1007\/s10207-023-00686-y"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"106438","DOI":"10.1016\/j.dib.2020.106438","article-title":"Datasets for phishing websites detection","volume":"33","author":"Fister","year":"2020","journal-title":"Data Brief"},{"key":"ref_52","unstructured":"Vrban\u010di\u010d, G. (2022, November 30). Phishing Websites Dataset. Available online: https:\/\/data.mendeley.com\/datasets\/72ptz43s9v\/1."},{"key":"ref_53","first-page":"590","article-title":"A systematic literature review on phishing website detection techniques","volume":"35","author":"Safi","year":"2023","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"7919","DOI":"10.1109\/TSMC.2020.2982226","article-title":"AUC-Based Extreme Learning Machines for Supervised and Semi-Supervised Imbalanced Classification","volume":"51","author":"Wang","year":"2021","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/978-3-642-02172-5_57","article-title":"Index of Balanced Accuracy: A Performance Measure for Skewed Class Distributions","volume":"Volume 5524","author":"Mollineda","year":"2009","journal-title":"Proceedings of the Pattern Recognition and Image Analysis: 4th Iberian Conference"},{"key":"ref_56","first-page":"1","article-title":"Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning","volume":"18","author":"Nogueira","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref_57","unstructured":"van den Berg, T. (2022, November 16). imbDRL: Imbalanced Classification with Deep Reinforcement Learning. Available online: https:\/\/github.com\/Denbergvanthijs\/imbDRL."},{"key":"ref_58","unstructured":"van der Walt, S., and Millman, J. (July, January 28). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference, Austin, TX, USA."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy","volume":"585","author":"Harris","year":"2020","journal-title":"Nature"},{"key":"ref_60","unstructured":"Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W. (2016). OpenAI Gym. arXiv."},{"key":"ref_61","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2023, January 18). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online: https:\/\/www.tensorflow.org\/."},{"key":"ref_62","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Yerima, S.Y., and Alzaylaee, M.K. (2020, January 19\u201321). High Accuracy Phishing Detection Based on Convolutional Neural Networks. Proceedings of the 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS), Riyadh, Saudi Arabia.","DOI":"10.1109\/ICCAIS48893.2020.9096869"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/78.650093","article-title":"Bidirectional recurrent neural networks","volume":"45","author":"Schuster","year":"1997","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_67","unstructured":"He, H., Bai, Y., Garcia, E.A., and Li, S. (2008, January 1\u20138). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong, China."},{"key":"ref_68","first-page":"111","article-title":"Classification of imbalance data using tomek link (t-link) combined with random under-sampling (rus) as a data reduction method","volume":"1","author":"Elhassan","year":"2016","journal-title":"Glob. J. Technol. Optim."},{"key":"ref_69","unstructured":"Kubat, M., and Matwin, S. (1997, January 8\u201312). Addressing the curse of imbalanced training sets: One-sided selection. Proceedings of the Fourteenth International Conference on Machine Learning (ICML \u201997) Citeseer, San Francisco, CA, USA."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Johnson, J.M., and Khoshgoftaar, T.M. (August, January 30). Deep Learning and Data Sampling with Imbalanced Big Data. Proceedings of the 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI), Los Angeles, CA, USA.","DOI":"10.1109\/IRI.2019.00038"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1007\/s10796-020-10022-7","article-title":"The effects of data sampling with deep learning and highly imbalanced big data","volume":"22","author":"Johnson","year":"2020","journal-title":"Inf. Syst. Front."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/12\/6\/118\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:51:29Z","timestamp":1760125889000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/12\/6\/118"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,9]]},"references-count":71,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["computers12060118"],"URL":"https:\/\/doi.org\/10.3390\/computers12060118","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,9]]}}}