Statistical Model Checking of Common Attack Scenarios on Blockchain

TL;DR

This paper applies statistical model checking to verify blockchain security against real-world attack scenarios, identifying vulnerabilities and proposing mitigation strategies.

Contribution

It introduces a novel approach using statistical model checking to analyze and verify blockchain vulnerabilities in specific attack scenarios.

Findings

Successful modeling of DNS attack, double-spending, and consensus delay scenarios.

Identification of vulnerabilities and potential mitigation strategies.

Validation of the effectiveness of the proposed verification approach.

Abstract

Blockchain technology has developed significantly over the last decade. One of the reasons for this is its sustainability architecture, which does not allow modification of the history of committed transactions. That means that developers should consider blockchain vulnerabilities and eliminate them before the deployment of the system. In this paper, we demonstrate a statistical model checking approach for the verification of blockchain systems on three real-world attack scenarios. We build and verify models of DNS attack, double-spending with memory pool flooding, and consensus delay scenario. After that, we analyze experimental results and propose solutions to avoid these kinds of attacks.