在基于区块链的智能应用中采用机器学习

Vishal Suthar, V. Bansal, C. Reddy, J. L. Arias-Gonzáles, Devendra Singh, D. P. Singh
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引用次数: 0

摘要

近年来,区块链技术(BT)的发展使其成为一项独特的、革命性的、受欢迎的创新。BT的分散数据库优先考虑信息的安全性和保密性,其中的共识过程确保了数据的有效性和安全性。然而,它带来了新的安全问题,包括多数攻击和双重支出。需要使用加密货币敏感数据进行数据分析以解决上述问题。这些数据集的分析突出了最近开发的技术的价值,如机器学习(ML)。机器学习使用合理数量的数据来生成准确的预测。在机器学习中,数据交换和可靠性对于提高结果的准确性至关重要。这两种技术(ML和BT)的融合结果可能相当精确。在这项研究中,我们对机器学习(ML)的使用进行了深入的研究,以加强基于bt的智能系统的安全性。对基于区块链的网络的攻击可以使用各种经典的机器学习(ML)方法进行分析,包括卷积神经网络(CNN)、长短期记忆(LSTM)、聚类、Bagging和支持向量机(SVM) (LSTM)。我们还讨论了这两种技术如何在智能城市、国家电网、医疗和自主飞行器(uav)等一些先进领域一起使用。然后探讨了未来研究面临的困难和问题。最后,在深入分析的基础上进行了研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Adoption in Blockchain-Based Smart Applications
The development of blockchain technology (BT) in recent years has made it a distinctive, revolutionary, and popular innovation. Information security and confidentiality are prioritised by the decentralised database in BT. Additionally, the consensus process in it ensures the validity and security of the data. However, it brings up fresh security concerns including majority assault and the double expenditures. Data analytics using cryptocurrency sensitive data are needed to address the aforementioned problems. These dataset' analytics highlight the value of recently developed techniques such as machine learning (ML). ML uses a reasonable quantity of data to generate accurate predictions. In ML, data exchange and dependability are essential to enhancing the precision of outcomes. Results from the fusion of these two technologies (ML and BT) may be quite exact. In this research, we give a thorough investigation into the use of machine learning (ML) to strengthen the security of BT-based intelligent systems. The assaults on a blockchain-based network may be analysed using a variety of classic machine learning (ML) approaches, including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Clustering, Bagging, and Support Vector Machines (SVM) (LSTM).We also discuss how the two technologies may be used together in a number of advanced areas, including smart urban, the national grid, medicine, and autonomous aerial vehicles (UAVs). The difficulties and concerns facing future research are then examined. Finally, a study based with a thorough analysis is offered.
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