Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Implementing Ensemble Machine Learning Techniques for Fraud Detection in Blockchain Ecosystem Subject The topic of the resource Block Chain; Ensemble ML; Fraud Detection Description An account of the resource A new era of digital innovation, notably in the area of financial transactions, has been conducted in by the rise pertaining to block-chain technology. Although the decentralized nature of blockchain technology renders it prone to fraud, it has been praised for its capacity to offer a safe and transparent platform for financial transactions. The integrity of the entire blockchain network may be compromised by fraudulent transactions, which may also damage user and stakeholder trust. This study aims to assess machine learning's efficacy in detecting fraudulent transactions within blockchain networks and identifying the most effective model. To achieve its objectives, this study used a combination of data collection, data preprocessing, and machine learning techniques. The data used in this study was dataset of blockchain transactions and pre-processed using techniques such as feature engineering and normalization. Then trained and evaluated using several machine learning models, including Logistic Regression (LR), Naive Bayes (NB), SVM, XGboost, LightGBM, Random Forest(RF), and Stacking, in order to determine their effectiveness in detecting fraudulent transactions. XGBoost demonstrated the highest accuracy of 0.944 in the stacking model, establishing it as the top-performing model, closely followed by Light GBM. The study's discoveries offer significant practical implications for advancing fraud detection methods in blockchain networks. By pinpointing the most efficient machine learning model and crucial predictive fraud features, this research provides vital insights for refining precise detection algorithms, enhancing blockchain network security, and broadening their reliability across various applications. 2023 IEEE. Creator An entity primarily responsible for making the resource Josephine H.V.L.; Kumar P.T.; Joy J.; Mansurali A. Source A related resource from which the described resource is derived 2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 1980-1985. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICACRS58579.2023.10404402" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICACRS58579.2023.10404402</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185371169&amp;doi=10.1109%2FICACRS58579.2023.10404402&amp;partnerID=40&amp;md5=c3238789f036f2457a782c15e625a977" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185371169&amp;doi=10.1109%2fICACRS58579.2023.10404402&amp;partnerID=40&amp;md5=c3238789f036f2457a782c15e625a977</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835034023-5 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Josephine H.V.L., Business analytics, School of business and Management, Christ University, Karnataka, Bangalore, India; Kumar P.T., Lean Operations and Systems., School of business and management, Christ University, Karnataka, Bangalore, India; Joy J., Lean Operations and Systems., School of business and management, Christ University, Karnataka, Bangalore, India; Mansurali A., Department of Management, Central University of Tamil Nadu, Tamil Nadu, Thiruvarur, India