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区块链安全增强:一种针对混合共识算法和机器学习技术的方法

K Venkatesan^1,2, Syarifah Bahiyah Rahayu^3,4

¹Cyber Security & Digital Industrial Revolution Centre, National Defence University Malaysia (UPNM), Kuala Lumpur, Malaysia.

Scientific reports

|January 11, 2024

概括

本研究介绍了整合机器学习 (ML) 的混合共识算法,以增强区块链对网络攻击的安全性. 这些新的方法提高了网络的稳定性,并使智能威胁检测成为可能.

科学领域:

区块链技术区块链技术
网络安全网络安全
机器学习机器学习

背景情况:

分布式系统中的共识协议在达成协议方面面临挑战,并且容易受到网络攻击.
现有的区块链安全机制需要针对不断变化的威胁采取有效的预防措施.
之前的研究强调了在去中心化网络中加强安全的关键需求.

研究的目的:

提出混合共识算法,将机器学习 (ML) 技术结合起来,以提高区块链安全性.
解决现有的共识协议中的漏洞,并提高它们对网络攻击的抵御力.
探索ML的整合,用于预测网络攻击,异常检测和共识机制中的特征提取.

主要方法:

机器学习技术与混合共识算法的集成,如权威证明利工作 (DPoSW),利和工作证明 (PoSW),CASBFT证明 (PoCASBFT) 和权威拜占庭证据 (DBPoS).
在ProximaX区块链平台上展示拟议方法的有效性.
基于机器学习的混合共识模型对安全,信任,稳定性和能源效率的评估.

主要成果:

拟议的框架展示了一个能效的机制,增强了安全性,并适应了动态的网络条件.
混合方法利用ML来改进网络攻击预测,异常检测和特征提取,优化共识协议.
这项研究验证了分散网络中的ML集成共识算法的有效性,改善了决策和威胁预防.

结论:

机器学习集成的混合共识算法为增强区块链安全性,信任性和稳定性提供了一个有希望的解决方案.
拟议的框架提供了一种节能和适应性机制,用于检测和防止分散网络中的安全威胁.
需要解决可扩展性,延迟和资源需求等挑战,才能成功地在现实世界中实施基于ML的混合共识模型.

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