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Augmenting zero trust architecture to endpoints using blockchain: A <scp>state‐of‐the‐art</scp> review

Lampis Alevizos, Vinh Thong Ta, Mahmoud Hashem Eiza

2021Security and Privacy74 citationsDOIOpen Access PDF

Abstract

Abstract With the purpose of defending against lateral movement in today's borderless networks, zero trust architecture (ZTA) adoption is gaining momentum. With a full‐scale ZTA implementation, it is unlikely that adversaries will be able to spread through the network starting from a compromised endpoint. However, the already authenticated and authorized session of a compromised endpoint can be leveraged to carry out limited, though malicious, activities ultimately rendering the endpoints the Achilles heel of ZTA. To effectively detect such attacks, distributed collaborative intrusion detection systems with an attack scenario‐based approach have been developed. Nonetheless, advanced persistent threats have demonstrated their ability to bypass this approach with a high success ratio. As a result, adversaries can pass undetected or potentially alter the detection logging mechanisms to achieve a stealthy presence. Recently, blockchain technology has demonstrated solid use cases in the cyber security domain. In this paper, motivated by the convergence of ZTA and blockchain‐based intrusion detection and prevention, we examine how ZTA can be augmented onto endpoints. Namely, we perform a state‐of‐the‐art review of ZTA models, real‐world architectures with a focus on endpoints, and blockchain‐based intrusion detection systems. We discuss the potential of blockchain's immutability fortifying the detection process and identify open challenges as well as potential solutions and future directions.

Topics & Concepts

BlockchainComputer scienceIntrusion detection systemComputer securityRendering (computer graphics)Session (web analytics)ArchitectureComputer networkArtificial intelligenceWorld Wide WebArtVisual artsNetwork Security and Intrusion DetectionAdvanced Malware Detection TechniquesSpam and Phishing Detection