FAWPA: A FAW Attack Protection Algorithm Based on the Behavior of Blockchain Miners

Abstract

Blockchain has become one of the key techniques for the security of the industrial internet. However, the blockchain is vulnerable to FAW (Fork after Withholding) attacks. To protect the industrial internet from FAW attacks, this paper proposes a novel FAW attack protection algorithm (FAWPA) based on the behavior of blockchain miners. Firstly, FAWPA performs miner data preprocessing based on the behavior of the miners. Then, FAWPA proposes a behavioral reward and punishment mechanism and a credit scoring model to obtain cumulative credit value with the processed data. Moreover, we propose a miner’s credit classification mechanism based on fuzzy C-means (FCM), which combines the improved Aquila optimizer (AO) with strong solving ability. That is, FAWPA combines the miner’s accumulated credit value and multiple attack features as the basis for classification, and optimizes cluster center selection by simulating Aquila’s predation behavior. It can improve the solution update mechanism in different optimization stages. FAWPA can realize the rapid classification of miners’ credit levels by improving the speed of identifying malicious miners. To evaluate the protective effect of the target mining pool, FAWPA finally establishes a mining pool and miner revenue model under FAW attack. The simulation results show that FAWPA can thoroughly and efficiently detect malicious miners in the target mining pool. FAWPA also improves the recall rate and precision rate of malicious miner detection, and it improves the cumulative revenue of the target mining pool. The proposed algorithm performs better than ND, RSCM, AWRS, and ICRDS.

K-Anonymity Privacy Protection Algorithm for Multi-Dimensional Data against Skewness and Similarity Attacks

“…Therefore, KAPP considers clustering multi-dimensional data based on sensitive data. At the same time, the fuzzy C-means clustering algorithm can realize a flexible partition of sample points by calculating the membership degree of each sample point to all cluster centers [ 32 ]. Compared with the hard partition of other clusters, this clustering algorithm is more suitable for multi-dimensional data clustering.…”

“…The probability parameters and weight parameters are determined by experiments. The fuzzy weight e is determined by relevant literature [ 32 ], and other parameters are consistent with AVOA [ 33 ]. We replicate skewness and similarity attacks in the experiment and attack the anonymized data.…”

An Evolutionary Game Theory-Based Method to Mitigate Block Withholding Attack in Blockchain System

“…It is part of a public chain of blockchain systems [8] using the PoW consensus algorithm to maintain data consistency in decentralized distributed systems [9]. The public chain faces four kinds of attacks: BWH [10], fork after block withholding (FAW) [11], selfish mining [12] and 51% computing power [12]. A BWH attack is an attack mode in which malicious nodes obtain illegal gains by satisfying partial proof of work by honest miners.…”