However, the analysis of Ethereum data at the present stage is mostly based on the statistical characteristics of Ethereum nodes and lacks analysis of the transaction behavior between them. In this paper, we apply machine learning in Ethereum analysis for the first time and cluster users and ...
The class and probability formulas shown below are used to determine the Gini index of each branch on a node, which identifies the most likely branches. K-Nearest neighbor The K-Nearest Neighbor (KNN) clustering technique is commonly used in the data science sector to categorize datasets. The ...
Ethereum analysis via node clustering. In Proceedings of the Network and System Security: 13th International Conference, NSS 2019, Sapporo, Japan, 15–18 December 2019; Proceedings 13. Springer: Berlin/Heidelberg, Germany, 2019; pp. 114–129. [Google Scholar] Lannoo, K.; Parlour, R. Anti-...
In order to detect malicious phishing accounts hidden behind the address, a detailed analysis of the transaction behavior of the account is required [10]. Early research mainly conducted behavior clustering based on transaction behavior combined with heuristic conditions to analyze transaction rules [11...
Each node’s computational and storage requirements grow with the blockchain, potentially leading to performance degradation and decreased efficiency. Extensive Energy Consumption: Traditional blockchain consensus mechanisms, such as Proof-of-Work (PoW), require significant computational power and energy ...
However, the existing approaches have different imple- mentation environments and environment use cases, posing a challenge in comparative analysis. Hence, a uniform testbed was constructed, where the core methods of blockchain, security features, and resource management of the existing approaches were ...