The popularity and amazing attractiveness of cryptocurrencies, and especially Bitcoin, absorb countless enthusiasts daily. 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 present a new method for detecting anomalies in Bitcoin with more appropriate efficiency. For this purpose, in this study, the diagnosis of the collective anomaly was used, and instead of diagnosing the anomaly of individual addresses and wallets, the anomaly of users was examined, and the anomaly was more visible among users who had multiple wallets. In addition to using the collective anomaly detection method in this study, the Trimmed_Kmeans algorithm was used for clustering and the proposed method succeeded in identifying 14 users who had committed theft, fraud, and hack with 26 addresses in 9 cases. Compared to previous works, which detected a maximum of 7 addresses in 5 cases of fraud, the proposed method has performed well. Therefore, the proposed method, by presenting a new approach, in addition to reducing the processing power to extract features, succeeded in detecting abnormal users and also was able to find more transactions and addresses committed a scam.
翻译:加密的普及性和惊人的吸引力,特别是比特币,每天都吸收无数的爱好者。虽然链链技术能防止欺诈行为,但它无法单独发现欺诈行为。总是有难以想象的欺诈行为,因此有必要使用异常现象探测方法来查明异常和欺诈行为。本研究的主要目的是提出一种新的方法,以更适当的效率探测比特币中的异常现象,特别是比特币中的异常现象。为此,本研究使用了对集体异常现象的诊断,而不是诊断个人地址和钱包的异常现象,对用户的异常现象进行了检查,而不同之处在拥有多个钱包的用户中更为明显。除了在这项研究中使用集体异常检测方法外,还使用Trimmed_K means算法进行集群,拟议方法成功地查明了在9起案件中实施盗窃、欺诈和黑入了26个地址的14个用户。与以前的工作相比,在5起欺诈案件中发现最多7个地址,拟议的方法已经很好地运行。因此,拟议的方法通过展示新的手段,通过检测更多的能力提取和加工,还成功找到了一种不正常的处理方法。