{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T05:07:48Z","timestamp":1743138468093,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031632105"},{"type":"electronic","value":"9783031632112"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-63211-2_27","type":"book-chapter","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T14:02:22Z","timestamp":1718892142000},"page":"362-374","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Synthetic Data Generation and Impact Analysis of Machine Learning Models for Enhanced Credit Card Fraud Detection"],"prefix":"10.1007","author":[{"given":"Ahmed Abdullah","family":"Khaled","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md Mahmudul","family":"Hasan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shareeful","family":"Islam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Spyridon","family":"Papastergiou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haralambos","family":"Mouratidis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"key":"27_CR1","unstructured":"What\u2019s credit card fraud detection and why is it important? https:\/\/www.capitalone.com\/learn-grow\/privacy-security\/credit-card-fraud-detection\/"},{"key":"27_CR2","unstructured":"2023 Credit Card Fraud Report. https:\/\/www.security.org\/digital-safety\/credit-card-fraud-report\/"},{"key":"27_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102239","volume":"105","author":"S Varga","year":"2021","unstructured":"Varga, S., Brynildsen, J., Franke, U.: Cyber-threat perception and risk management in the Swedish financial sector. Comput. Secur. 105, 102239 (2021). https:\/\/doi.org\/10.1016\/j.cose.2021.102239","journal-title":"Comput. Secur."},{"key":"27_CR4","doi-asserted-by":"publisher","first-page":"3034","DOI":"10.1109\/ACCESS.2022.3232287","volume":"11","author":"SK Hashemi","year":"2023","unstructured":"Hashemi, S.K., Mirtaheri, S.L., Greco, S.: Fraud detection in banking data by machine learning techniques. IEEE Access 11, 3034\u20133043 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2022.3232287","journal-title":"IEEE Access"},{"key":"27_CR5","doi-asserted-by":"publisher","unstructured":"Najadat, H., Altiti, O., Aqouleh, A.A., Younes, M.: Credit card fraud detection based on machine and deep learning. In: 2020 11th International Conference on Information and Communication Systems (ICICS), pp. 204\u2013208. IEEE (2020). https:\/\/doi.org\/10.1109\/ICICS49469.2020.239524","DOI":"10.1109\/ICICS49469.2020.239524"},{"key":"27_CR6","doi-asserted-by":"publisher","first-page":"145725","DOI":"10.1109\/ACCESS.2019.2945858","volume":"7","author":"L Gong","year":"2019","unstructured":"Gong, L., Jiang, S., Jiang, L.: Tackling class imbalance problem in software defect prediction through cluster-based over-sampling with filtering. IEEE Access 7, 145725\u2013145737 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2945858","journal-title":"IEEE Access"},{"key":"27_CR7","doi-asserted-by":"publisher","unstructured":"Pozzolo, A.D., Caelen, O., Johnson, R.A., Bontempi, G.: Calibrating probability with undersampling for unbalanced classification. In: 2015 IEEE Symposium Series on Computational Intelligence, Cape Town, South Africa, pp. 159\u2013166. IEEE (2015). https:\/\/doi.org\/10.1109\/SSCI.2015.33","DOI":"10.1109\/SSCI.2015.33"},{"key":"27_CR8","doi-asserted-by":"publisher","first-page":"131920","DOI":"10.1109\/ACCESS.2020.3009753","volume":"8","author":"Y-S Jeon","year":"2020","unstructured":"Jeon, Y.-S., Lim, D.-J.: PSU: particle stacking undersampling method for highly imbalanced big data. IEEE Access 8, 131920\u2013131927 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3009753","journal-title":"IEEE Access"},{"key":"27_CR9","doi-asserted-by":"publisher","first-page":"73103","DOI":"10.1109\/ACCESS.2021.3079701","volume":"9","author":"Y-R Chen","year":"2021","unstructured":"Chen, Y.-R., Leu, J.-S., Huang, S.-A., Wang, J.-T., Takada, J.-I.: Predicting default risk on peer-to-peer lending imbalanced datasets. IEEE Access 9, 73103\u201373109 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3079701","journal-title":"IEEE Access"},{"key":"27_CR10","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Huang, S.: Credit card fraud detection via deep learning method using data balance tools. In: 2020 International Conference on Computer Science and Management Technology (ICCSMT), pp. 133\u2013137. IEEE (2020). https:\/\/doi.org\/10.1109\/ICCSMT51754.2020.00033","DOI":"10.1109\/ICCSMT51754.2020.00033"},{"key":"27_CR11","doi-asserted-by":"publisher","unstructured":"Awoyemi, J.O., Adetunmbi, A.O., Oluwadare, S.A.: Credit card fraud detection using machine learning techniques: a comparative analysis. In: 2017 International Conference on Computing Networking and Informatics (ICCNI), pp. 1\u20139. IEEE (2017). https:\/\/doi.org\/10.1109\/ICCNI.2017.8123782","DOI":"10.1109\/ICCNI.2017.8123782"},{"key":"27_CR12","doi-asserted-by":"publisher","first-page":"165286","DOI":"10.1109\/ACCESS.2021.3134330","volume":"9","author":"E Ileberi","year":"2021","unstructured":"Ileberi, E., Sun, Y., Wang, Z.: Performance evaluation of machine learning methods for credit card fraud detection using SMOTE and AdaBoost. IEEE Access 9, 165286\u2013165294 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3134330","journal-title":"IEEE Access"},{"key":"27_CR13","doi-asserted-by":"publisher","unstructured":"Itoo, F., Meenakshi, Singh, S.: Comparison and analysis of logistic regression, Na\u00efve Bayes and KNN machine learning algorithms for credit card fraud detection. Int. J. Inf. Technol. 13(4), 1503\u20131511 (2021). https:\/\/doi.org\/10.1007\/S41870-020-00430-Y\/METRICS","DOI":"10.1007\/S41870-020-00430-Y\/METRICS"},{"key":"27_CR14","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.future.2024.02.015","volume":"156","author":"R Kumar","year":"2024","unstructured":"Kumar, R., Aljuhani, A., Javeed, D., Kumar, P., Shareeful Islam, A.K.M., Islam, N.: Digital twins-enabled zero touch network: a smart contract and explainable AI integrated cybersecurity framework. Fut. Gener. Comput. Syst. 156, 191\u2013205 (2024)","journal-title":"Fut. Gener. Comput. Syst."}],"container-title":["IFIP Advances in Information and Communication Technology","Artificial Intelligence Applications and Innovations"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-63211-2_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T14:05:09Z","timestamp":1718892309000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-63211-2_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031632105","9783031632112"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-63211-2_27","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"type":"print","value":"1868-4238"},{"type":"electronic","value":"1868-422X"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"21 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Artificial Intelligence Applications and Innovations","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Corfu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ifipaiai.org\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}