{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:38:05Z","timestamp":1743129485264,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"electronic","value":"9783031248016"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-24801-6_36","type":"book-chapter","created":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T18:07:14Z","timestamp":1675274834000},"page":"511-522","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Graph-Based Approach to\u00a0Detect Anomalies Based on\u00a0Shared Attribute Values"],"prefix":"10.1007","author":[{"given":"Steffen","family":"Brauer","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Fisichella","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gianluca","family":"Lax","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlo","family":"Romeo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonia","family":"Russo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"36_CR1","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.ins.2019.05.042","volume":"557","author":"F Carcillo","year":"2019","unstructured":"Carcillo, F., Le Borgne, Y.A., Caelen, O., Kessaci, Y., Obl\u00e9, F., Bontempi, G.: Combining unsupervised and supervised learning in credit card fraud detection. Inf. Sci. 557, 317\u2013331 (2019). https:\/\/doi.org\/10.1016\/j.ins.2019.05.042","journal-title":"Inf. Sci."},{"key":"36_CR2","doi-asserted-by":"publisher","unstructured":"Eswaran, D., Faloutsos, C., Guha, S., Mishra, N.: Spotlight: detecting anomalies in streaming graphs. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), pp. 1378\u20131386. Association for Computing Machinery, New York, NY, USA (2018). https:\/\/doi.org\/10.1145\/3219819.3220040","DOI":"10.1145\/3219819.3220040"},{"issue":"1","key":"36_CR3","doi-asserted-by":"publisher","first-page":"49","DOI":"10.33215\/sjom.v5i1.770","volume":"5","author":"Z Faraji","year":"2022","unstructured":"Faraji, Z.: A review of machine learning applications for credit card fraud detection with a case study. SEISENSE J. Manag. 5(1), 49\u201359 (2022)","journal-title":"SEISENSE J. Manag."},{"issue":"4","key":"36_CR4","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/s00799-021-00308-9","volume":"22","author":"M Fisichella","year":"2021","unstructured":"Fisichella, M.: Unified approach to retrospective event detection for event- based epidemic intelligence. Int. J. Digit. Libr. 22(4), 339\u2013364 (2021). https:\/\/doi.org\/10.1007\/s00799-021-00308-9","journal-title":"Int. J. Digit. Libr."},{"issue":"1","key":"36_CR5","first-page":"2030","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2030\u20132096 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"36_CR6","unstructured":"J.P. Morgan Merchant Services: Key Trends to Drive Your Payments Strategy (2021). https:\/\/www.jpmorgan.com\/content\/dam\/jpm\/merchant-services\/insights\/e-commerce\/key-trends-to-drive-your-payments-strategy.pdf. Accessed 18 Apr 2022"},{"key":"36_CR7","doi-asserted-by":"publisher","first-page":"28210","DOI":"10.1109\/ACCESS.2020.2972009","volume":"8","author":"SN Kalid","year":"2020","unstructured":"Kalid, S.N., Ng, K.H., Tong, G.K., Khor, K.C.: A multiple classifiers system for anomaly detection in credit card data with unbalanced and overlapped classes. IEEE Access 8, 28210\u201328221 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2972009","journal-title":"IEEE Access"},{"issue":"1","key":"36_CR8","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1214\/aoms\/1177729694","volume":"22","author":"S Kullback","year":"1951","unstructured":"Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79\u201386 (1951)","journal-title":"Ann. Math. Stat."},{"key":"36_CR9","series-title":"Proceedings of the International Neural Networks Society","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/978-3-030-16841-4_8","volume-title":"Recent Advances in Big Data and Deep Learning","author":"B Lebichot","year":"2020","unstructured":"Lebichot, B., Le Borgne, Y.-A., He-Guelton, L., Obl\u00e9, F., Bontempi, G.: Deep-learning domain adaptation techniques for credit cards fraud detection. In: Oneto, L., Navarin, N., Sperduti, A., Anguita, D. (eds.) INNSBDDL 2019. PINNS, vol. 1, pp. 78\u201388. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-16841-4_8"},{"key":"36_CR10","doi-asserted-by":"publisher","first-page":"93010","DOI":"10.1109\/ACCESS.2019.2927266","volume":"7","author":"S Makki","year":"2019","unstructured":"Makki, S., Assaghir, Z., Taher, Y., Haque, R., Hacid, M.S., Zeineddine, H.: An experimental study with imbalanced classification approaches for credit card fraud detection. IEEE Access 7, 93010\u201393022 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2927266","journal-title":"IEEE Access"},{"key":"36_CR11","doi-asserted-by":"crossref","unstructured":"Mathew, J.C., Nithya, B., Vishwanatha, C., Shetty, P., Priya, H., Kavya, G.: An analysis on fraud detection in credit card transactions using machine learning techniques. In: 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), pp. 265\u2013272. IEEE (2022)","DOI":"10.1109\/ICAIS53314.2022.9742830"},{"key":"36_CR12","unstructured":"Micenkov\u00e1, B., McWilliams, B., Assent, I.: Learning outlier ensembles: the best of both worlds-supervised and unsupervised. In: Proceedings of the ACM SIGKDD 2014 Workshop on Outlier Detection and Description under Data Diversity (ODD2), pp. 51\u201354. Citeseer, New York, USA (2014)"},{"issue":"3","key":"36_CR13","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.1109\/18.135663","volume":"38","author":"DN Politis","year":"1992","unstructured":"Politis, D.N.: Moving average processes and maximum entropy. IEEE Trans. Inf. Theory 38(3), 1174\u20131177 (1992)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"36_CR14","first-page":"1","volume":"9","author":"A Pumsirirat","year":"2018","unstructured":"Pumsirirat, A., Yan, L.: Credit card fraud detection using deep learning based on auto-encoder and restricted Boltzmann machine. Int. J. Adv. Comput. Sci. Appl. 9, 1\u20138 (2018)","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"36_CR15","doi-asserted-by":"publisher","unstructured":"Raghavan, P., Gayar, N.E.: Fraud detection using machine learning and deep learning. In: 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), pp. 334\u2013339 (2019). https:\/\/doi.org\/10.1109\/ICCIKE47802.2019.9004231","DOI":"10.1109\/ICCIKE47802.2019.9004231"},{"key":"36_CR16","doi-asserted-by":"publisher","unstructured":"Rajora, S., et al.: A comparative study of machine learning techniques for credit card fraud detection based on time variance. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1958\u20131963 (2018). https:\/\/doi.org\/10.1109\/SSCI.2018.8628930","DOI":"10.1109\/SSCI.2018.8628930"},{"key":"36_CR17","doi-asserted-by":"publisher","first-page":"14277","DOI":"10.1109\/ACCESS.2018.2806420","volume":"6","author":"K Randhawa","year":"2018","unstructured":"Randhawa, K., Loo, C.K., Seera, M., Lim, C.P., Nandi, A.K.: Credit card fraud detection using AdaBoost and majority voting. IEEE Access 6, 14277\u201314284 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2806420","journal-title":"IEEE Access"},{"issue":"4","key":"36_CR18","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.3390\/s21041026","volume":"21","author":"AM Said","year":"2021","unstructured":"Said, A.M., Yahyaoui, A., Abdellatif, T.: Efficient anomaly detection for smart hospital IoT systems. Sensors 21(4), 1026 (2021)","journal-title":"Sensors"},{"key":"36_CR19","doi-asserted-by":"publisher","first-page":"105895","DOI":"10.1016\/j.asoc.2019.105895","volume":"86","author":"J Vanhoeyveld","year":"2019","unstructured":"Vanhoeyveld, J., Martens, D., Peeters, B.: Value-added tax fraud detection with scalable anomaly detection techniques. Appl. Soft Comput. 86, 105895 (2019). https:\/\/doi.org\/10.1016\/j.asoc.2019.105895","journal-title":"Appl. Soft Comput."},{"key":"36_CR20","doi-asserted-by":"publisher","first-page":"65092","DOI":"10.1109\/ACCESS.2022.3184309","volume":"10","author":"R Younis","year":"2022","unstructured":"Younis, R., Fisichella, M.: FLY-SMOTE: re-balancing the non-IID IoT edge devices data in federated learning system. IEEE Access 10, 65092\u201365102 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3184309","journal-title":"IEEE Access"},{"key":"36_CR21","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.ins.2019.05.023","volume":"557","author":"X Zhang","year":"2019","unstructured":"Zhang, X., Han, Y., Xu, W., Wang, Q.: HOBA: a novel feature engineering methodology for credit card fraud detection with a deep learning architecture. Inf. Sci. 557, 305\u2013316 (2019). https:\/\/doi.org\/10.1016\/j.ins.2019.05.023","journal-title":"Inf. Sci."}],"container-title":["Communications in Computer and Information Science","Applied Intelligence and Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-24801-6_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T14:07:42Z","timestamp":1683554862000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-24801-6_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031248016"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-24801-6_36","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"2 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applied Intelligence and Informatics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Reggio Calabria","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apii2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.aii2022.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Proconf (https:\/\/proconf.org\/)","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"108","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}