WiMi Hologram Cloud, a leading global Hologram Augmented Reality (“AR”) Technology provider, announced a Trimmed K-Means algorithm for detecting crypto-wallet fraud on the Bitcoin network. It combines symmetry and asymmetry in computer and engineering sciences to provide a novel solution for crypto-wallet fraud on the Bitcoin network. The technology not only improves detection efficiency but also identifies anomalous behavior more accurately, providing a more secure trading environment for Bitcoin investors.
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This algorithm has a wide range of potential for practical applications. First, through the collective anomaly detection method, anomalous behaviors can be identified more efficiently on the Bitcoin network, improving the overall security of the network. Second, the Trimmed K-Means algorithm is used to reduce the processing cost of feature extraction, making it more practical. In addition, the algorithm not only detects fraudulent behavior but also provides a more reliable trading environment for Bitcoin investment.
The core of WiMi’s Trimmed K-Means algorithm is the symmetry and asymmetry of the blockchain. Symmetry, which means that a complete record of transactions is kept at each node, ensures decentralization. Asymmetry, on the other hand, manifests itself in the relative anonymity of each transaction participant, which provides potential room for fraudsters to hide. The algorithm better identifies anomalous behavior by analyzing the overall behavior of the user community.
Second, collective anomaly detection methods are used to detect anomalous behavior on the Bitcoin network more efficiently. Collective anomaly detection is a method that identifies anomalies in individual behaviors by analyzing overall behavioral patterns. On the Bitcoin network, it is relatively common for users to have multiple wallets, and fraudulent behavior is often reflected in the behavioral patterns of an entire group of users. Unlike traditional single-address and wallet anomaly detection methods, this algorithm captures potential crypto-wallet fraud more comprehensively by focusing on users’ anomalous behaviors. This approach not only improves detection accuracy, but also reduces false reports.
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In order to better distinguish user groups, the Trimmed K-Means algorithm is used for cluster analysis. The K-means algorithm is usually widely used in cluster analysis, but it is easily disturbed when dealing with data containing outliers. The Trimmed K-Means algorithm improves the accuracy of clustering by removing outliers from the dataset, making the differentiation of user groups more The Trimmed K-Means algorithm improves the accuracy of clustering by removing outliers from the data set, making the distinction between user groups more refined. This algorithm is more suitable for the complex and variable user data on the Bitcoin network, further improving detection efficiency and accuracy. The workflow of the algorithm can be summarized in the following steps:
Data acquisition and pre-processing: Acquire user transaction data from the Bitcoin network and pre-process the data, including removing outliers data.
Collective anomaly detection: By analyzing the overall behavioral patterns of the user population, collective anomaly detection methods are used to identify anomalies in the overall behavior, rather than focusing only on anomalies in individual addresses or wallets.
Cluster analysis: Clustering users with abnormal behavior using the Trimmed K-Means algorithm to better distinguish different user groups and improve the differentiation of abnormal behavior.
Result output: Output detection results to present potential crypto wallet fraud to system administrators or users for real-time warning.
Fraud in crypto wallets on the Bitcoin network has long been a concern in the industry, and the successful development of WiMi, an anomaly detection algorithm, provides a new way to address this issue. As the Bitcoin network continues to grow and the cryptocurrency market continues to expand, the need for fraud prevention will continue to increase, and WiMi’s Trimmed K-Means algorithm provides strong support for cryptocurrency security, and is expected to be used in many more areas in the future. WiMi’s technical team will continue to optimize and upgrade the algorithm in order to cope with escalating fraudulent methods and contribute to the healthy development of the Bitcoin network.
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