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The increasingly frequent illegal transactions hinder the secure transactions of Ethereum, and the anonymity of electronic currency makes it difficult to track and analyze illegal transactions. This paper used the transaction data of the Ethereum platform as the data source, the marked illegal account and unmarked normal account data set as the training set, and the characteristic attributes of the transaction data as the construction basis. Account for the overall forecast. The process uses the T-SNE algorithm to realize the dimensionality reduction and visualization of transaction features, adopts multiple cross-validation, and introduces the SHAP Value factor to judge the positive and negative attributes of the feature. The prediction effect accuracy rate of the established model reaches 94.29%. The evaluation metric for the area (AUC) value reached 0.9846. The proposed scheme can more accurately predict the illegal behavior on the Ethereum trading platform, and will effectively improve the blockchain-based trading environment. [ABSTRACT FROM AUTHOR]

Aiming at the security problems caused by fraudulent accounts in the blockchain, this paper proposed a machine learning-based fraud account detection and feature analysis model. It took the real on-chain data on Ethereum into feature extraction and used it as the data source of the model. It obtained the optimal model by comparing different machine learning methods and performing iterative training to obtain the best predictive model. At the same time, it introduced SHAP value to analyze data characteristics. The experimental results show that the fraud account detection model based on XGBoost achieves 0. 205, 0. 084 and 0. 833 in RMSE, MAE and R², which is better than the other comparison models. Combined with the SHAP value, it identifies the key factors that predict fraudulent accounts to provide decision-making reference for the transaction security of blockchain. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the master node in the practical Byzantine fault tolerance(PBFT) consensus mechanism may go wrong and cause a large amount of waste of communication resources, this paper proposed a consensus mechanism optimization scheme based on trust dynamic incentives, ITPBFT(incentive trust-PBFT). It evaluated the consensus behavior of nodes, added a dynamic incentive mechanism, introduced a node level mechanism, and selected a node with a high degree of trust as the master node. The simulation experiment and analysis of the optimization scheme verify that the mechanism can effectively improve the fault tolerance of the consensus mechanism, reduce the consensus time, increase the throughput of the system, and improve the stability and recoverability during network fluctuations. This consensus mechanism can effectively improve the efficiency of consensus and avoid waste of communication resources. [ABSTRACT FROM AUTHOR]

Signing a transaction with the private key stored in the blockchain wallet is the only way to trade on the block chain, and the private key stored in the wallet can cause irreparable damage if lost or compromised. Current mainstream wallet management schemes centrally store wallets in a single location or rely on the involvement of trusted third parties to withstand a single point of attack. This paper proposed a blockchain wallet protection scheme that based on threshold elliptic digital signature without trusted center. In the absence of trust center participation, it participated cooperate to generate public and private key fragments, and participated that exceed the threshold can cooperate to sign transactions, that could resist single-point attacks effectively, and protect blockchain wallet. With the analysis on security, this scheme meets the security of anti-counterfeiting, anonymity, robust, non-traceability and irrevocability. With the efficiency analysis, the scheme complete signature just needs constant rounds. The proposed scheme can protect the security of blockchain wallet with less signature time increasing. [ABSTRACT FROM AUTHOR]

The polar codes proposed by Arikan caused much attention due to their simple structure. As a high-performance channel encoding, polar codes would achieve good performances once the code length N was greater than 210. On the basis of the successive cancelation (SC) decoding algorithm, the decoding latency increased with the increase of code length. By analysis of the position of frozen bits and the SC decoder architecture, this paper proposed an improved SC decoding algorithm, which effectively reduced the latency of the traditional SC decoder algorithm. The algorithm improved the decoding delay by 50%compared to the original. What’s more, it further improves the error correction capability of the decoding algorithm by using the succession cancellation single-bit-flipping. [ABSTRACT FROM AUTHOR]