Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 1304-8899 Cukurova University 10.35379/cusosbil.1669958 Econometrics (Other) Ekonometri (Diğer) BİTCOİN İŞLEMLERİNDE HİLELİ FAALİYET ÖRÜNTÜLERİNİN TESPİTİ: ŞÜPHELİ CÜZDAN DAVRANIŞLARININ ANALİZİ DETECTING FRAUDULENT ACTIVITY PATTERNS IN BITCOIN TRANSACTIONS: AN ANALYSIS OF SUSPICIOUS WALLET BEHAVIORS https://orcid.org/0000-0001-7138-2972 Balcıoğlu Yavuz Selim Doğuş Üniversitesi 04 28 2026 35 04 04 2025 09 17 2025 Copyright © 2013, Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 2013 Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi

Bu çalışma, cüzdan düzeyindeki davranışların analizi yoluyla Bitcoin işlemlerindeki potansiyel dolandırıcılık faaliyetlerini belirlemek için örüntü tabanlı bir yaklaşım geliştirmekte ve uygulamaktadır. Toplam 8.526 Bitcoin cüzdanından oluşan bir veri kümesini inceleyerek, beş şüpheli işlem modelinden en az birini sergileyen 72 cüzdan (%0,84) tespit ettik: tek seferlik yüksek değerli transferler, potansiyel karıştırma hizmetleri, önemli cüzdanların aniden boşaltılması, anormal işlem oranları ve büyük hareketsiz cüzdanlar. Sayıları az olmasına rağmen, bu şüpheli cüzdanlar 777,15 BTC'yi kontrol ediyordu ve bu da veri kümesindeki toplam Bitcoin'in %9,39'unu temsil ediyordu. İstatistiksel analiz, şüpheli ve şüpheli olmayan cüzdanlar arasında önemli farklılıklar olduğunu ortaya koymuştur; şüpheli cüzdanlar 11,9 kat daha yüksek ortalama işlem değerleri, 12,3 kat daha yüksek ortalama bakiyeler ve önemli ölçüde daha yüksek işlem sıklığı göstermektedir. Çapraz desen analizi, şüpheli cüzdanların %26,4'ünün aynı anda birden fazla şüpheli desen sergilediğini ortaya koyarak koordineli suç stratejilerine işaret etmiştir. Belirlenen kalıplar, kara para aklama, fidye yazılımı ödeme işlemleri ve yasadışı fon depolama gibi bilinen kripto para birimi destekli suçlarla uyumludur. Bu araştırma, şüpheli işlem modellerinin bir tipolojisini oluşturarak, bunların finansal etkilerini ölçerek ve kripto para ağlarında potansiyel olarak hileli faaliyetlerin tespitini iyileştirebilecek gelişmiş izleme sistemleri için bir çerçeve sağlayarak kripto para güvenliğine katkıda bulunmaktadır.

This study develops and applies a pattern-based approach to identify potentially fraudulent activity in Bitcoin transactions through the analysis of wallet-level behaviors. Examining a dataset of 8,526 Bitcoin wallets, we identified 72 wallets (0.84%) exhibiting at least one of five suspicious transaction patterns: one-time high-value transfers, potential mixing services, sudden draining of significant wallets, abnormal transaction rates, and large dormant wallets. Despite their small number, these suspicious wallets controlled 777.15 BTC, representing 9.39% of the total Bitcoin in the dataset. Statistical analysis revealed significant differences between suspicious and non-suspicious wallets, with suspicious wallets showing 11.9 times higher average transaction values, 12.3 times higher average balances, and substantially greater transaction frequencies. Cross-pattern analysis found that 26.4% of suspicious wallets exhibited multiple suspicious patterns simultaneously, suggesting coordinated criminal strategies. The identified patterns align with known cryptocurrency-facilitated crimes such as money laundering, ransomware payment processing, and illicit fund storage. This research contributes to cryptocurrency security by establishing a typology of suspicious transaction patterns, quantifying their financial impact, and providing a framework for enhanced monitoring systems that could improve detection of potentially fraudulent activity across cryptocurrency networks.

 Bitcoin fraud detection cryptocurrency security suspicious transaction patterns money laundering blockchain forensic Bitcoin dolandırıcılığı tespiti kripto para birimi güvenliği şüpheli işlem kalıpları kara para aklama blok zinciri adli tıp Arner, D. W., Auer, R., & Frost, J. (2020). Stablecoins: Risks, potential and regulation. BIS Quarterly Review, September 2020, 81-98. Arnold, N. A., Zhong, P., Ba, C. T., Steer, B., Mondragon, R., Cuadrado, F., Lambiotte, R., & Clegg, R. G. (2024). Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks. Scientific Reports, 14(1), Article 26569. https://doi.org/10.1038/s41598-024-75348-7 Ashfaq, T., Khalid, R., Yahaya, A. S., Aslam, S., Azar, A. T., Alsafari, S., & Hameed, I. A. (2022). A machine learning and blockchain based efficient fraud detection mechanism. Sensors, 22(19), Article 7162. https://doi.org/10.3390/s22197162 Asiri, A., & Somasundaram, K. (2025). Graph convolution network for fraud detection in bitcoin transactions. Scientific Reports, 15(1), Article 1076. Bartoletti, M., Pes, B., & Serusi, S. (2021). Data mining for detecting bitcoin Ponzi schemes. In Proceedings of the 2018 Crypto Valley Conference on Blockchain Technology (pp. 75-84). IEEE. https://doi.org/10.1109/CVCBT.2018.00014 Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213-238. https://doi.org/10.1257/jep.29.2.213 Chen, W., Zheng, Z., Cui, J., Ngai, E., Zheng, P., & Zhou, Y. (2020). Detecting Ponzi schemes on Ethereum: Towards healthier blockchain technology. In Proceedings of the 2018 World Wide Web Conference (pp. 1409-1418). ACM. https://doi.org/10.1145/3178876.3186046 Conti, M., Kumar, E. S., Lal, C., & Ruj, S. (2018). A survey on security and privacy issues of bitcoin. IEEE Communications Surveys & Tutorials, 20(4), 3416-3452. https://doi.org/10.1109/COMST.2018.2842460 Coutinho, K., Khairwal, N., & Wongthongtham, P. (2023). Towards a truly decentralized blockchain framework for remittance. Journal of Risk and Financial Management, 16(4), Article 240. https://doi.org/10.3390/jrfm16040240 Deuber, D., Gruber, J., Humml, M., Ronge, V., & Scheler, N. (2024). Argumentation schemes for blockchain deanonymisation. FinTech, 3(2), 236-248. https://doi.org/10.3390/fintech3020014 Ferretti, S., D'Angelo, G., & Ghini, V. (2025). Enhancing anti-money laundering frameworks: An application of graph neural networks in cryptocurrency transaction classification. IEEE Access, 13, 1-15. Goldsmith, D., Grauer, K., & Shmalo, Y. (2020). Analyzing cryptocurrency market and its anomalies. Journal of Computational Social Science, 3(2), 365-396. https://doi.org/10.1007/s42001-020-00067-8 Koronaios, A., & Koloniari, G. (2025). Graph-based bitcoin fraud detection using variational graph autoencoders and supervised learning. Procedia Computer Science, 257, 817-825. https://doi.org/10.1016/j.procs.2025.01.078 Óskarsdóttir, M., & Mallett, J. (2021). Strangely mined bitcoins: Empirical analysis of anomalies in the bitcoin blockchain transaction network. PLoS ONE, 16(9), Article e0258001. https://doi.org/10.1371/journal.pone.0258001 Ouyang, S., Bai, Q., Feng, H., & Hu, B. (2024). Bitcoin money laundering detection via subgraph contrastive learning. Entropy, 26(3), Article 211. https://doi.org/10.3390/e26030211 Paquet-Clouston, M., Haslhofer, B., & Dupont, B. (2019). Ransomware payments in the Bitcoin ecosystem. Journal of Cybersecurity, 5(1), Article tyz003. https://doi.org/10.1093/cybsec/tyz003 Pérez-Cano, V., & Jurado, F. (2025). Fraud detection in cryptocurrency networks—An exploration using anomaly detection and heterogeneous graph transformers. Future Internet, 17(1), Article 44. https://doi.org/10.3390/fi17010044 Teichmann, F., & Falker, M. C. (2020). Money laundering through cryptocurrencies. In F. Teichmann (Ed.), Artificial intelligence: Anthropogenic nature vs. social origin (pp. 500-511). Springer International Publishing. https://doi.org/10.1007/978-3-030-39974-4_38 Toyoda, K., Ohtsuki, T., & Mathiopoulos, P. T. (2019). Identification of high yielding investment programs in Bitcoin via transactions pattern analysis. In Proceedings of the 2018 IEEE Global Wireless Summit (pp. 202-207). IEEE. https://doi.org/10.1109/GWS.2018.8686650 Trozze, A., Kamps, J., Akartuna, E. A., Hetzel, F. J., Kleinberg, B., Davies, T., & Johnson, S. D. (2022). Cryptocurrencies and future financial crime. Crime Science, 11(1), Article 1. https://doi.org/10.1186/s40163-021-00163-8 Turner, A. B., McCombie, S., & Uhlmann, A. J. (2020). Analysis techniques for illicit bitcoin transactions. Frontiers in Computer Science, 2, Article 600596. https://doi.org/10.3389/fcomp.2020.600596 Velankar, S., Valecha, H., & Maji, S. (2021). Bitcoin fraud detection using machine learning. In Proceedings of the 2020 IEEE International Conference on Advances in Computing, Communication & Materials (pp. 205-210). IEEE. https://doi.org/10.1109/ICACCM50413.2020.9213015 Victor, F. (2020). Address clustering heuristics for Ethereum. In J. Bonneau & N. Heninger (Eds.), Financial cryptography and data security (pp. 617-633). Springer. https://doi.org/10.1007/978-3-030-51280-4_33 Weber, M., Domeniconi, G., Chen, J., Weidele, D. K. I., Bellei, C., Robinson, T., & Leiserson, C. E. (2019). Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics. In Proceedings of the ACM SIGKDD Workshop on Anomaly Detection in Finance (pp. 1-8). ACM. https://doi.org/10.1145/3338486.3340729 Wu, J., Yuan, Q., Lin, D., You, W., Chen, W., Chen, C., & Zheng, Z. (2021). Who are the phishers? Phishing scam detection on Ethereum via network embedding. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(2), 1156-1166. https://doi.org/10.1109/TSMC.2020.3016821 Zheng, Z., Zhou, B., & Song, Y. (2025). Temporal-aware graph attention network for cryptocurrency transaction fraud detection. arXiv. https://doi.org/10.48550/arXiv.2506.21382