{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:32:45Z","timestamp":1773246765553,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,2,5]],"date-time":"2022-02-05T00:00:00Z","timestamp":1644019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The popularity and remarkable attractiveness of cryptocurrencies, especially Bitcoin, absorb countless enthusiasts every day. Although Blockchain technology prevents fraudulent behavior, it cannot detect fraud on its own. There are always unimaginable ways to commit fraud, and the need to use anomaly detection methods to identify abnormal and fraudulent behaviors has become a necessity. The main purpose of this study is to use the Blockchain technology of symmetry and asymmetry in computer and engineering science to present a new method for detecting anomalies in Bitcoin with more appropriate efficiency. In this study, a collective anomaly approach was used. Instead of detecting the anomaly of individual addresses and wallets, the anomaly of users was examined. In addition to using the collective anomaly detection method, the trimmed_Kmeans algorithm was used for clustering. The results of this study show the anomalies are more visible among users who had multiple wallets. The proposed method revealed 14 users who had committed fraud, including 26 addresses in 9 cases, whereas previous works detected a maximum of 7 addresses in 5 cases of fraud. The suggested approach, in addition to reducing the processing overhead for extracting features, detect more abnormal users and anomaly behavior.<\/jats:p>","DOI":"10.3390\/sym14020328","type":"journal-article","created":{"date-parts":[[2022,2,6]],"date-time":"2022-02-06T20:40:18Z","timestamp":1644180018000},"page":"328","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["A Collective Anomaly Detection Technique to Detect Crypto Wallet Frauds on Bitcoin Network"],"prefix":"10.3390","volume":"14","author":[{"given":"Mohammad Javad","family":"Shayegan","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, University of Science and Culture, Bahar St., Shahid Qamushi St., Ashrafi Esfahani Bulvar, Tehran 1461968151, Iran"}]},{"given":"Hamid Reza","family":"Sabor","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, University of Science and Culture, Bahar St., Shahid Qamushi St., Ashrafi Esfahani Bulvar, Tehran 1461968151, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1919-3407","authenticated-orcid":false,"given":"Mueen","family":"Uddin","sequence":"additional","affiliation":[{"name":"School of Digital Science, University Brunei Darussalam, Gadong BE1410, Brunei"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4958-2043","authenticated-orcid":false,"given":"Chin-Ling","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Changchun Sci-Tech University, Changchun 130600, China"},{"name":"School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China"},{"name":"Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,5]]},"reference":[{"key":"ref_1","unstructured":"Fischer, A.M. 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