defensecchain:网络威胁情报共享和防御联盟区块链

Soumya Purohit, P. Calyam, Songjie Wang, RajaniKanth Yempalla, Justin Varghese
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引用次数: 7

摘要

云托管应用程序容易受到针对性攻击,如DDoS、高级持续性威胁、威胁服务可用性的加密劫持。目前,威胁信息共享和防御方法需要多个领域/实体之间的合作和信任。有必要建立分布式信任机制,以允许这种集体防御。在本文中,我们提出了一种新的威胁情报共享和防御系统,即“防御链”,允许组织进行基于激励和可信的合作,以减轻网络攻击的影响。我们的解决方案采用联盟区块链平台来获取威胁数据,并选择合适的对等体来帮助检测和缓解攻击。我们通过使用“检测质量”和“缓解质量”指标的声誉估计方案,提出了一个与同行建立和维持联盟的经济模型。我们对defensecchain实现的评估实验是在Hyperledger Composer的开放云测试平台和模拟环境中进行的。我们的研究结果表明,在选择最合适的检测器和缓解器对等体方面,DefenseChain系统总体上比最先进的决策方案表现更好。此外,我们还展示了我们的DefenseChain在检测时间、缓解时间和攻击复发率等指标方面实现了更好的性能权衡。最后,我们的验证结果表明,我们的防御链可以有效地识别合理/不合理的服务提供者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DefenseChain: Consortium Blockchain for Cyber Threat Intelligence Sharing and Defense
Cloud-hosted applications are prone to targeted attacks such as DDoS, advanced persistent threats, cryptojacking which threaten service availability. Recently, methods for threat information sharing and defense require co-operation and trust between multiple domains/entities. There is a need for mechanisms that establish distributed trust to allow for such a collective defense. In this paper, we present a novel threat intelligence sharing and defense system, namely “DefenseChain”, to allow organizations to have incentive-based and trustworthy co-operation to mitigate the impact of cyber attacks. Our solution approach features a consortium Blockchain platform to obtain threat data and select suitable peers to help with attack detection and mitigation. We propose an economic model for creation and sustenance of the consortium with peers through a reputation estimation scheme that uses ‘Quality of Detection’ and ‘Quality of Mitigation’ metrics. Our evaluation experiments with DefenseChain implementation are performed on an Open Cloud testbed with Hyperledger Composer and in a simulation environment. Our results show that the DefenseChain system overall performs better than state-of-the-art decision making schemes in choosing the most appropriate detector and mitigator peers. In addition, we show that our DefenseChain achieves better performance trade-offs in terms of metrics such as detection time, mitigation time and attack reoccurence rate. Lastly, our validation results demonstrate that our DefenseChain can effectively identify rational/irrational service providers.
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