College of Information Science and Technology, Zhejiang Shuren University, Hangzhou 310015, China.
School of Computer and Artificial Intelligence, Changzhou University, Changzhou 213164, China.
Sensors (Basel). 2022 Jul 4;22(13):5032. doi: 10.3390/s22135032.
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.
区块链已成为工业互联网安全的关键技术之一。然而，区块链容易受到 FAW（ withholding 后分叉）攻击。为了保护工业互联网免受 FAW 攻击，本文提出了一种基于区块链矿工行为的新型 FAW 攻击保护算法（FAWPA）。首先，FAWPA 基于矿工的行为对矿工数据进行预处理。然后，FAWPA 提出了一种行为奖励和惩罚机制以及信用评分模型，利用处理后的数据获得累积信用值。此外，我们提出了一种基于模糊 C 均值（FCM）的矿工信用分类机制，该机制结合了具有强大求解能力的改进 Aquila 优化器（AO）。也就是说，FAWPA 将矿工的累积信用值和多个攻击特征结合起来作为分类的基础，并通过模拟 Aquila 的捕食行为来优化聚类中心的选择。它可以改善不同优化阶段的解更新机制。FAWPA 通过提高识别恶意矿工的速度，可以快速对矿工的信用等级进行分类。为了评估目标挖矿池的保护效果，FAWPA 最后在 FAW 攻击下建立了一个挖矿池和矿工收益模型。仿真结果表明，FAWPA 可以彻底有效地检测目标挖矿池中的恶意矿工。FAWPA 还提高了恶意矿工检测的召回率和准确率，并提高了目标挖矿池的累积收益。所提出的算法在 ND、RSCM、AWRS 和 ICRDS 上表现更好。
