{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:06:46Z","timestamp":1776442006291,"version":"3.51.2"},"reference-count":32,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,10]],"date-time":"2023-09-10T00:00:00Z","timestamp":1694304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Efficiently and accurately identifying fraudulent credit card transactions has emerged as a significant global concern along with the growth of electronic commerce and the proliferation of Internet of Things (IoT) devices. In this regard, this paper proposes an improved algorithm for highly sensitive credit card fraud detection. Our approach leverages three machine learning models: K-nearest neighbor, linear discriminant analysis, and linear regression. Subsequently, we apply additional conditional statements, such as \u201cIF\u201d and \u201cTHEN\u201d, and operators, such as \u201c&gt;\u201c and \u201c&lt;\u201c, to the results. The features extracted using this proposed strategy achieved a recall of 1.0000, 0.9701, 1.0000, and 0.9362 across the four tested fraud datasets. Consequently, this methodology outperforms other approaches employing single machine learning models in terms of recall.<\/jats:p>","DOI":"10.3390\/s23187788","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T10:42:49Z","timestamp":1694428969000},"page":"7788","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Credit Card Fraud Detection: An Improved Strategy for High Recall Using KNN, LDA, and Linear Regression"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4668-320X","authenticated-orcid":false,"given":"Jiwon","family":"Chung","sequence":"first","affiliation":[{"name":"School of Cybersecurity, Korea University, Seoul 02841, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5183-5927","authenticated-orcid":false,"given":"Kyungho","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Cybersecurity, Korea University, Seoul 02841, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,10]]},"reference":[{"key":"ref_1","unstructured":"(2023, July 28). Fraud\u2014Quick Search Results. Available online: https:\/\/www.oed.com\/search\/dictionary\/?scope=Entries&q=fraud."},{"key":"ref_2","first-page":"145","article-title":"Credit card fraud detection in the era of disruptive technologies: A systematic review","volume":"35","author":"Cherif","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_3","unstructured":"Davidson, A. (2023, July 28). Card Not Present Fraud Is Skyrocketing. National Association of Federally-Insured Credit Unions. Available online: https:\/\/www.nafcu.org\/nafcuservicesnafcu-services-blog\/card-not-present-fraud-skyrocketing."},{"key":"ref_4","unstructured":"Security.org Team (2023, July 28). 2023 Credit Card Fraud Report. Security.org. Available online: https:\/\/www.security.org\/digital-safety\/credit-card-fraud-report\/."},{"key":"ref_5","unstructured":"Department of Financial Payment, Bank of Korea (2023, July 28). Payment and Settlement Survey Data: Current Status and Implications of Discussions on Cross-Border Payment and Settlement Systems in Major Countries. Available online: https:\/\/www.bok.or.kr\/portal\/bbs\/B0000232\/view.do?nttId=10068027&menuNo=200706."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1109\/TCSS.2018.2856910","article-title":"Transaction Fraud Detection Based on Total Order Relation and Behavior Diversity","volume":"5","author":"Zheng","year":"2018","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neucom.2011.02.021","article-title":"Improved competitive learning neural networks for network intrusion and fraud detection","volume":"75","author":"Lei","year":"2011","journal-title":"Neurocomputing"},{"key":"ref_8","unstructured":"Prasetiyo, B., Alamsyah Muslim, M.A., and Baroroh, N. (2021). Journal of Physics: Conference Series, IOP Publishing."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gupta, A., Anand, A., and Hasija, Y. (2021, January 2\u20134). Recall-based Machine Learning approach for early detection of Cervical Cancer. Proceedings of the 2021 6th International Conference for Convergence in Technology (I2CT), Maharashtra, India.","DOI":"10.1109\/I2CT51068.2021.9418099"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Murugappan, M. (2011, January 27\u201328). Electromyogram signal based human emotion classification using KNN and LDA. Proceedings of the IEEE International Conference on System Engineering and Technology, Shah Alam, Malaysia.","DOI":"10.1109\/ICSEngT.2011.5993430"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Starzacher, A., and Rinner, B. (2008, January 15\u201318). Evaluating KNN, LDA and QDA Classification for embedded online Feature Fusion. Proceedings of the 2008 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Sydney, NSW, Australia.","DOI":"10.1109\/ISSNIP.2008.4761967"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lopez-Bernal, D., Balderas, D., Ponce, P., and Molina, A. (2021). Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems. Future Internet, 13.","DOI":"10.3390\/fi13080193"},{"key":"ref_13","first-page":"6","article-title":"A novel idea for credit card fraud detection using decision tree","volume":"161","author":"Save","year":"2017","journal-title":"Int. J. Comput. Appl."},{"key":"ref_14","first-page":"1","article-title":"Credit card fraud detection using naive Bayesian and c4. 5 decision tree classifiers","volume":"4","author":"Husejinovic","year":"2020","journal-title":"Credit Card Fraud Detect. Using Naive Bayesian C"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"\u015eahin, Y.G., and Duman, E. (2011, January 16\u201318). Detecting credit card fraud by decision trees and support vector machines. Proceedings of the International MultiConference of Engineers and Computer Scientists 2011, Hong Kong, China.","DOI":"10.1109\/INISTA.2011.5946108"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xuan, S., Liu, G., Li, Z., Zheng, L., Wang, S., and Jiang, C. (2018, January 27\u201329). Random forest for credit card fraud detection. Proceedings of the 2018 IEEE 15th international conference on networking, sensing and control (ICNSC), Zhuhai, China.","DOI":"10.1109\/ICNSC.2018.8361343"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kumar, M.S., Soundarya, V., Kavitha, S., Keerthika, E.S., and Aswini, E. (2019, January 21). Credit card fraud detection using random forest algorithm. Proceedings of the 2019 3rd International Conference on Computing and Communications Technologies (ICCCT), Gangtok, India.","DOI":"10.1109\/ICCCT2.2019.8824930"},{"key":"ref_18","unstructured":"Lopez-Rojas, E. (2023, July 29). Synthetic Financial Datasets For Fraud Detection. Kaggle. Available online: https:\/\/www.kaggle.com\/datasets\/ealaxi\/paysim1."},{"key":"ref_19","unstructured":"Shenoy, K. (2023, July 29). Credit Card Transactions Fraud Detection Dataset. Kaggle. Available online: https:\/\/www.kaggle.com\/datasets\/kartik2112\/fraud-detection."},{"key":"ref_20","unstructured":"Yadav, S. (2023, July 29). Credit-Card-Fraud Detection-Imbalanced-Dataset. Kaggle. Available online: https:\/\/www.kaggle.com\/datasets\/dark06thunder\/credit-card-dataset."},{"key":"ref_21","unstructured":"IEEE Computational Intelligence Society (2023, July 29). IEEE-CIS Fraud Detection. Kaggle. Available online: https:\/\/www.kaggle.com\/competitions\/ieee-fraud-detection."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sahin, Y., and Duman, E. (2011, January 15). Detecting credit card fraud by ANN and logistic regression. Proceedings of the 2011 international symposium on innovations in intelligent systems and applications, Istanbul, Turkey.","DOI":"10.1109\/INISTA.2011.5946108"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"13941","DOI":"10.1007\/s10489-022-03244-6","article-title":"Zero-day ransomware attack detection using deep contractive autoencoder and voting based ensemble classifier","volume":"52","author":"Zahoora","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"105670","DOI":"10.1016\/j.engappai.2022.105670","article-title":"RepuTE: A soft voting ensemble learning framework for reputation-based attack detection in fog-IoT milieu","volume":"118","author":"Verma","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Malik, E.F., Khaw, K.W., Belaton, B., Wong, W.P., and Chew, X. (2022). Credit card fraud detection using a new hybrid machine learning architecture. Mathematics, 10.","DOI":"10.3390\/math10091480"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jiang, S., Dong, R., Wang, J., and Xia, M. (2023). Credit Card Fraud Detection Based on Unsupervised Attentional Anomaly Detection Network. Systems, 11.","DOI":"10.3390\/systems11060305"},{"key":"ref_27","unstructured":"Akshaya, V., Sathyapriya, M., Ranjini Devi, R., and Sivanantham, S. (2022). Intelligent Systems and Sustainable Computing: Proceedings of ICISSC 2021, Springer Nature."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cai, Q., and He, J. (2022, January 14\u201316). Credit Payment Fraud detection model based on TabNet and Xgboot. Proceedings of the 2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE), Guangzhou, China.","DOI":"10.1109\/ICCECE54139.2022.9712842"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"96852","DOI":"10.1109\/ACCESS.2022.3205416","article-title":"A proposed model for card fraud detection based on Catboost and deep neural network","volume":"10","author":"Nguyen","year":"2022","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cochrane, N., Gomez, T., Warmerdam, J., Flores, M., Mccullough, P., Weinberger, V., and Pirouz, M. (2021, January 27\u201330). Pattern Analysis for Transaction Fraud Detection. Proceedings of the IEEE Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC51732.2021.9376045"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","article-title":"K-nearest neighbor","volume":"4","author":"Peterson","year":"2009","journal-title":"Scholarpedia"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1002\/(SICI)1097-4571(199401)45:1<12::AID-ASI2>3.0.CO;2-L","article-title":"The relationship between recall and precision","volume":"45","author":"Buckland","year":"1994","journal-title":"J. Am. Soc. Inf. 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