Anomaly Detection Model Over Blockchain Electronic Transactions

Electronic transactions with cryptocurrency systems based on blockchain in our days have become very popular due to the good reputation of this technology. However, that good reputation cannot deny the serious anomalies and the risks that can cause these cryptocurrencies. In this work, we propose a...

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Bibliographic Details
Published in2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC) pp. 895 - 900
Main Authors SAYADI, Sirine, BEN REJEB, Sonia, CHOUKAIR, Zied
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2019
Subjects
Anomaly detection
Bitcoin
Blockchain
Clustering algorithms
Computer crime
K-means
Machine learning
Machine learning algorithms
One Class SVM
Support vector machines
Transaction
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ISSN2376-6506
DOI10.1109/IWCMC.2019.8766765

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Summary:Electronic transactions with cryptocurrency systems based on blockchain in our days have become very popular due to the good reputation of this technology. However, that good reputation cannot deny the serious anomalies and the risks that can cause these cryptocurrencies. In this work, we propose a new model for anomaly detection over bitcoin electronic transactions. We used in our proposal two machine learning algorithms, namely the One Class Support Vector Machines (OCSVM) algorithm to detect outliers and the K-Means algorithm in order to group the similar outliers with the same type of anomalies. We evaluated our work by generating detection results and we obtained high performance results on accuracy.
ISSN:2376-6506
DOI:10.1109/IWCMC.2019.8766765