Öz
Blockchain is an emerging technology which is also behind the Bitcoin digital money. Daily bitcoin transactions are increasing due to the popular and widespread investments. The increase of Bitcoin related datasets and this increased big dataset requires novel approaches and methods to analyze using data mining techniques. In addition, fluctuations and anomalies in the bitcoin prices could mean a great deal to economists and discovering anomalies in bitcoin prices is important. In this study, anomaly detection in Bitcoin prices is performed based on the change of Bitcoin price difference and the change of Bitcoin price difference in percentage with respect to previous day using 8-years of Bitcoin price dataset of the period of 2012-2019. First, the dataset is pre-processed and unnecessary columns are deleted. Then, 2 different datasets are created by using daily bitcoin prices, i.e. bitcoin price difference dataset and bitcoin price difference in percentage dataset. After that, for detecting anomalous price changes, DBSCAN algorithm and statistical method are used, and the performance of the algorithms are evaluated. The results show that the DBSCAN algorithm and statistical method successfully detects anomalies in bitcoin prices for both of the datasets. However, the DBSCAN algorithm performs better than the statistical method which could detect anomalies even they are close to the normal daily price changes. Also, in this study, bitcoin price difference dataset and bitcoin price difference in percentage dataset are compared and the differences of the results for both datasets and their reasons are explained.
NOT: Makalenin düzeltilmiş haline Alper Ecemis - Düzeltme ulaşabilirsiniz.
Anahtar Kelimeler
Bitcoin price Blockchain Data mining Anomaly detection DBSCAN Algorithm statistical approach
Kaynakça
- Agrawal, S., & Agrawal, J. (2015). Survey on Anomaly Detection using Data Mining Techniques. Procedia - Procedia Computer Science, 60, 708–713. https://doi.org/10.1016/j.procs.2015.08.220
- Baek, U.-J., Lee, M., Park, J., & Kim, M. (2019). DDoS Attack Detection on Bitcoin Ecosystem using. Ecosystem Using Deep-Learning. In 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS), 1–4.
- Bartoletti, M., Pes, B., & Serusi, S. (2018). Data mining for detecting Bitcoin Ponzi schemes. Crypto Valley Conference on Blockchain Technology (CVCBT), 75–84.
- Blockonomi news. (2019). Retrieved February 1, 2020, from https://blockonomi.com/mt-gox-hack/
- Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, Technology, and Governance. 29(2), 213–238.
- Çelik, M., Dadaşer-Çelik, F., & Dokuz, A. Ş. (2011). Anomaly detection in temperature data using DBSCAN algorithm. INISTA 2011 - 2011 International Symposium on INnovations in Intelligent SysTems and Applications, 91–95. https://doi.org/10.1109/INISTA.2011.5946052
- Chen, M., Gao, X. D., & Li, H. F. (2010). Parallel DBSCAN with Priority R-tree. ICIME 2010 - 2010 2nd IEEE International Conference on Information Management and Engineering, 3, 508–511. https://doi.org/10.1109/ICIME.2010.5477926
- Dokuz, A. Ş., Ecemiş, A., & Celik, M. (2019). Hourly , Daily , and Monthly Analysis of Big Dataset of Bitcoin Blocks. International Conference on Engineering Technologies (ICENTE’19).
- Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd, 96(34), 226–231. Id, F. S., Sun, X., Gao, J., Xu, L., Shen, H., & Cheng, X. (2019). Anomaly detection in Bitcoin market via price return analysis. 6(14).
- Investing. (2020). Retrieved February 1, 2020, from www.investing.com
- Khan, K., Rehman, S. U., Aziz, K., Fong, S., Sarasvady, S., & Vishwa, A. (2014). DBSCAN: Past, present and future. 5th International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2014, 232–238. https://doi.org/10.1109/ICADIWT.2014.6814687
- Lee, S., & Edu, T. S. (2016). Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods. ArXiv Preprint ArXiv:1611.03941.
- Melanie, S. (2017). Anticipating the Economic Benefits of Blockchain. Technology Innovation Management Review, 7(10), 6–13. https://doi.org/10.22215/timreview/1107
- Monamo, P., Marivate, V., & Twala, B. (2016). Unsupervised Learning for Robust Bitcoin Fraud Detection. Information Security for South Africa (ISSA), 129–134.
- Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. https://doi.org/10.1007/s10838-008-9062-0
- Ozekes, A., Celik, M., Ozkok, F. O., Komuscu, A. U., & Dadaser-celik, F. (2018). AutoVDBSCAN : An Automatic and Level-Wise Varied-Density Based Anomaly Detection Algorithm. 7th International Conference on Advanced Technologies (ICAT’18).
- Sas, C., & Mara, U. T. (2017). Design for trust: An exploration of the challenges and opportunities of bitcoin users. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 6499–6510.
- Sayadi, S., Rejeb, S. Ben, & Choukair, Z. (2019). Anomaly Detection Model Over Blockchain Electronic Transactions. 15th International Wireless Communications & Mobile Computing Conference (IWCMC), 895–900.
- Tan, P.-N. T., Steinbach, M., Anuj, K., & Kumar, V. (2005). Introduction to Data Mining.
Öz
Blokzincir, bitcoin dijital para biriminin de alt yapısını oluşturan yeni bir teknolojidir. Popüler ve yaygın yatırımlar sayesinde günlük gerçekleştirilen bitcoin işlem sayısı gün geçtikçe artmaktadır. Bitcoin verisi her geçen gün artmakta ve dolayısıyla artan büyük bitcoin verisinin analizi ve madenciliği için yeni veri madenciliği yöntemlerine ihtiyaç duyulmaktadır. Buna ek olarak, Bitcoin fiyatındaki dalgalanmalar ve anormal fiyat değişimleri ve bu değişimlerdeki anormalliklerin keşfi ekonomistler için büyük önem taşımaktadır. Bu çalışmada, 2012-2019 yıllarına ait 8 yıllık bitcoin fiyat veri kümesi kullanılarak bitcoin fiyat farkı ve bitcoin fiyatı yüzdesel farkı olmak üzere iki farklı veri kümesi oluşturulup, anormallik tespiti gerçekleştirilmiştir. Öncelikle veri kümesi ön işlem aşamasından geçirilerek gereksiz sütunlar çıkarılmıştır ve daha sonra günlük fiyat farkları kullanılarak veri setleri oluşturulup, DBSCAN algoritması ile anormallik tespiti yapılmıştır. Ayrıca bu çalışmada DBSCAN aloritmasının sonuçları istatistiksel yöntemin sonuçları ile karşılaştırılıp, tartışılmıştır. Sonuçlar incelendiğinde, DBSCAN algoritması ve istatistiksel metodun bitcoin fiyatlarındaki anormallikleri her iki veri kümesinde de başarıyla tespit edebildiği görülmüştür. Bununla birlikte DBSCAN algoritması normal günlük fiyat değişimlerine yakın olan anormak fiyat değişimlerini de keşfedebildiği için istatistiksel metottan daha iyi performans göstermiştir. Ayrıca, bu çalışmada bitcoin fiyat farkı veri kümesi ve bitcoin fiyatı yüzdesel farlı veri kümesi karşılaştırılmış ve her bir veri kümesi için olan sonuçlar ve sebepleri tartışılmıştır.
NOT: Makalenin düzeltilmiş haline Alper Ecemis - Düzeltme ulaşabilirsiniz.
Anahtar Kelimeler
Bitcoin fiyatı Blokzincir Veri madenciliği Anormallik tespiti DBSCAN algoritması İstatistiksel yöntem
Kaynakça
- Agrawal, S., & Agrawal, J. (2015). Survey on Anomaly Detection using Data Mining Techniques. Procedia - Procedia Computer Science, 60, 708–713. https://doi.org/10.1016/j.procs.2015.08.220
- Baek, U.-J., Lee, M., Park, J., & Kim, M. (2019). DDoS Attack Detection on Bitcoin Ecosystem using. Ecosystem Using Deep-Learning. In 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS), 1–4.
- Bartoletti, M., Pes, B., & Serusi, S. (2018). Data mining for detecting Bitcoin Ponzi schemes. Crypto Valley Conference on Blockchain Technology (CVCBT), 75–84.
- Blockonomi news. (2019). Retrieved February 1, 2020, from https://blockonomi.com/mt-gox-hack/
- Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, Technology, and Governance. 29(2), 213–238.
- Çelik, M., Dadaşer-Çelik, F., & Dokuz, A. Ş. (2011). Anomaly detection in temperature data using DBSCAN algorithm. INISTA 2011 - 2011 International Symposium on INnovations in Intelligent SysTems and Applications, 91–95. https://doi.org/10.1109/INISTA.2011.5946052
- Chen, M., Gao, X. D., & Li, H. F. (2010). Parallel DBSCAN with Priority R-tree. ICIME 2010 - 2010 2nd IEEE International Conference on Information Management and Engineering, 3, 508–511. https://doi.org/10.1109/ICIME.2010.5477926
- Dokuz, A. Ş., Ecemiş, A., & Celik, M. (2019). Hourly , Daily , and Monthly Analysis of Big Dataset of Bitcoin Blocks. International Conference on Engineering Technologies (ICENTE’19).
- Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd, 96(34), 226–231. Id, F. S., Sun, X., Gao, J., Xu, L., Shen, H., & Cheng, X. (2019). Anomaly detection in Bitcoin market via price return analysis. 6(14).
- Investing. (2020). Retrieved February 1, 2020, from www.investing.com
- Khan, K., Rehman, S. U., Aziz, K., Fong, S., Sarasvady, S., & Vishwa, A. (2014). DBSCAN: Past, present and future. 5th International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2014, 232–238. https://doi.org/10.1109/ICADIWT.2014.6814687
- Lee, S., & Edu, T. S. (2016). Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods. ArXiv Preprint ArXiv:1611.03941.
- Melanie, S. (2017). Anticipating the Economic Benefits of Blockchain. Technology Innovation Management Review, 7(10), 6–13. https://doi.org/10.22215/timreview/1107
- Monamo, P., Marivate, V., & Twala, B. (2016). Unsupervised Learning for Robust Bitcoin Fraud Detection. Information Security for South Africa (ISSA), 129–134.
- Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. https://doi.org/10.1007/s10838-008-9062-0
- Ozekes, A., Celik, M., Ozkok, F. O., Komuscu, A. U., & Dadaser-celik, F. (2018). AutoVDBSCAN : An Automatic and Level-Wise Varied-Density Based Anomaly Detection Algorithm. 7th International Conference on Advanced Technologies (ICAT’18).
- Sas, C., & Mara, U. T. (2017). Design for trust: An exploration of the challenges and opportunities of bitcoin users. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 6499–6510.
- Sayadi, S., Rejeb, S. Ben, & Choukair, Z. (2019). Anomaly Detection Model Over Blockchain Electronic Transactions. 15th International Wireless Communications & Mobile Computing Conference (IWCMC), 895–900.
- Tan, P.-N. T., Steinbach, M., Anuj, K., & Kumar, V. (2005). Introduction to Data Mining.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 1 Nisan 2020 |
| Yayımlandığı Sayı | Yıl 2020 Ejosat Özel Sayı 2020 (ARACONF) |
