Blockchain Security Analysis with Multi-Factor Authentication and

Multi-Signature Mechanisms

Weifeng Li

School of Informatics, Xiamen University, Xiamen, China

Keywords: Blockchain Security, Multi-Factor Authentication, Multi-Signature Mechanisms, Smart Contract.

Abstract: Blockchain technology is renowned for its decentralization and transparency but faces significant security

challenges, particularly in large-scale deployments where encryption and consensus mechanisms are

vulnerable to attacks. This study aims to bolster blockchain security by employing advanced techniques,

specifically focusing on multi-factor authentication (MFA) and multi-signature mechanisms. The research

adopts a comprehensive approach that integrates Mythril's static code analysis, JUnit's dynamic testing, and

Echidna's fuzz testing to identify and address vulnerabilities in smart contracts. Static code analysis is used to

detect common vulnerabilities, dynamic testing ensures module functionality, and fuzz testing uncovers edge-

case issues. The study demonstrates the effectiveness of MFA in mitigating risks associated with password

leakage through static and one-time passwords, while the multi-signature mechanism enhances security by

requiring multiple approvals for transactions. Experimental results on a publicly available smart contract

dataset reveal that these security enhancements substantially reduce security incidents and improve system

stability. These findings offer practical solutions for optimizing blockchain security and provide a solid

foundation for future research on safeguarding blockchain applications in complex scenarios.

1 INTRODUCTION

Blockchain technology, with its decentralization,

immutability, and transparency, has shown great

potential in finance, healthcare, and other fields.

However, security and privacy issues in large-scale

applications are gradually emerging. Vulnerabilities

in cryptographic algorithms and consensus

mechanisms on which blockchain relies may lead to

security threats such as double payments and

transaction tampering. Therefore, systematically

analyzing the security issues of blockchain

technology, identifying potential threats, and

exploring effective countermeasure strategies are of

great significance in safeguarding the security of

blockchain systems and promoting their wide

application (Zhang et.al, 2019).

In recent years, extensive research on blockchain

security has been conducted in academia and

industry, focusing on improving the system's

resistance to attacks through cryptographic

techniques, consensus algorithms, and security

protocols. Many studies have focused on improving

https://orcid.org/0009-0002-2306-4130

consensus mechanisms, enhancing the security of

smart contracts, and cryptographic security. For

example, the evolution of consensus algorithms such

as Proof of Work (PoW) and Proof of Stake (PoS)

(Ferdous et.al, 2021), as well as the introduction of

technologies such as non-interactive zero-knowledge

proofs and hash-chain storage, have significantly

improved the security and scalability of blockchains.

Joseph Bonneau et al. provided the first systematic

exposition of Bitcoin and other cryptocurrencies,

analyzed the anonymity problem, and reviewed the

privacy enhancement methods (Bonneau et.al, 2015).

Ghassan Karame systematically outlined and

analyzed the security provisioning of the blockchain

in Bitcoin, including risks and attacks in Bitcoin-like

digital currency systems (Karam, 2016). They also

described and evaluated mitigation strategies to

eliminate some of the risks. Mauro Conti et al

reviewed the security and privacy of Bitcoin,

including existing vulnerabilities that lead to various

security risks during the implementation of the

Bitcoin system (Conti et.al, 2018). Li et al.

investigated the security risks of popular blockchain

390

Li and W.

Blockchain Security Analysis with Multi-Factor Authentication and Multi-Signature Mechanisms.

DOI: 10.5220/0013524600004619

In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning (DAML 2024), pages 390-394

ISBN: 978-989-758-754-2

systems, reviewed cases of attacks on blockchains,

and analyzed the vulnerabilities exploited in these

cases(Li et.al, 2020). Dasgupta et al provide a detailed

categorization of blockchain security issues, covering

everything from vulnerabilities in cryptographic

operations to possible threats posed by quantum

computing (Dasgupta et.al, 2019). In addition, some

studies propose specific ways to counter these threats,

such as using more secure elliptic curve algorithms to

defend against cryptographic attacks (Zhang et.al,

2022). Other studies focus on security issues at the

blockchain network level, such as encryption of inter-

node communication and measures to prevent

Distributed Denial of Service (DDoS) attacks (Sousa

and Monteiro, 2018). As the application scenarios of

blockchain technology continue to expand, especially

in the fields of finance and the Internet of Things

(IoT), the requirements for its security are getting

higher and higher. Therefore, in-depth understanding

and solving the security problems of blockchain

technology in different application scenarios has

become an important direction of current research.

Therefore, addressing blockchain security in various

application scenarios has become a key research

direction.

The primary objective of this research is to

systematically analyze the security challenges

associated with blockchain technology and propose

effective methods to address these issues. This study

provides a comprehensive overview of security

threats, classified based on existing literature, and

emphasizes a comparative analysis of various

cryptographic algorithms and consensus

mechanisms. It further explores the security

assessment of smart contracts through methods such

as static code analysis, dynamic testing, and fuzz

testing. Additionally, the research highlights the

implementation of multi-signature and multi-factor

authentication (MFA) to bolster the security of

critical operations. The findings of this study reveal

that the choice of cryptographic algorithms, the

robustness of consensus mechanisms, and network-

level protection measures are crucial for enhancing

overall blockchain security. By focusing on these

areas, the study offers substantial security

improvements for practical blockchain applications

and serves as a valuable reference for future research

in blockchain security, particularly in complex

application scenarios.

2 METHODOLOGIES

2.1 Dataset Description and

Preprocessing

The dataset used in this study is mainly derived from

publicly available smart contract platforms, such as

Etherscan and smart contract repositories on GitHub

(Etherscan, 2015). The dataset contains multiple

types of smart contracts, such as token contracts,

decentralized financial protocols, and governance

system contracts. To ensure smooth experiments, the

data preprocessing is done by removing extraneous

data, standardizing the format of the contract code,

and ensuring its compatibility with security testing

tools such as Mythril and Echidna. In addition,

duplicate or deprecated contracts in the dataset are

filtered out to improve the accuracy of the

experiments.

2.2 The Proposed Methodology

The objective of this research is to enhance the

security and stability of blockchain smart contracts

through advanced technological tools. This study

integrates static code analysis, dynamic testing, and

fuzz testing, while also incorporating multi-signature

mechanisms and multi-factor authentication to

bolster security. The research methodology, outlined

in Figure 1, involves several key stages: identifying

security vulnerabilities, conducting code testing and

verification, and implementing security

enhancements. The process begins with static code

analysis to detect potential vulnerabilities. This is

followed by dynamic testing to assess contract

behavior under various conditions. Next, fuzz testing

is employed to verify the robustness of the contracts

by exposing them to a range of inputs. Finally, the

integration of multi-signature mechanisms and multi-

factor authentication is applied to further secure the

system.

Figure 1: Flowchart of the study for this model (Picture

credit: Original).

Blockchain Security Analysis with Multi-Factor Authentication and Multi-Signature Mechanisms

391

2.2.1 Static Code Analysis

Mythril is a powerful static analysis tool specifically

designed to analyze the security of smart contract

codes. Its main features include code syntax

checking, pattern matching, and symbolic execution

to effectively identify common vulnerabilities in

contracts, such as reentry attacks and integer

overflows. This study uses Mythril to perform an in-

depth security analysis of the contract, generate a

detailed vulnerability report, and fix the

vulnerabilities according to the recommendations in

the report. The advantage of Mythril lies in its ability

to identify potential vulnerabilities before the

execution of the contract, avoiding losses caused by

exposing the problems after the code goes live.

During the implementation process, this study first

inputs the contract into Mythril for static analysis, and

the generated vulnerability report provides a

reference for subsequent dynamic testing.

2.2.2 Dynamic Testing

Dynamic testing is critical to ensure that the code

behaves properly at runtime. This study adopted the

JUnit framework to conduct unit tests for different

modules of the smart contract. The advantage of JUnit

is that it can automatically run pre-written test cases

every time the code is changed so that new

vulnerabilities in the code can be captured promptly.

This paper wrote detailed test cases for each contract

module to ensure that each module works properly in

an independent environment and effectively reduces

the testing bias caused by external dependencies.

During the testing process, JUnit can help researcher

verify the boundary conditions and exception

handling of the contract to ensure its stability in real

applications.

2.2.3 Fuzz Testing

Fuzz testing is a random input testing method for

verifying the robustness of smart contracts. This

study uses the Echidna tool to generate a large

number of random inputs to test whether a smart

contract can satisfy predefined security properties.

The advantage of Echidna is that it can generate a

wide range of different random test cases, which

helps researcher to discover potential vulnerabilities

that cannot be covered by regular tests. This study

uses Echidna to perform fuzzing tests on several

smart contracts, and by analyzing the generated test

reports, this study has promptly identified and fixed

several boundary case vulnerabilities. The failure

cases provided by Echidna also provide valuable

references for further optimizing the contract design.

2.2.4 Security Enhancement

To further enhance the security of the system, this

study introduces multiple signature mechanisms and

MFA in the smart contract. The multi-signature

mechanism requires multiple authorized signers to

participate in the transaction or operation together,

and the transaction can only be executed after the set

signature threshold is reached. This study sets up a

signer manager class in the system to collect and

verify the public and private keys of the signers, thus

ensuring the security of the transaction. Multi-factor

authentication, on the other hand, provides users with

double protection by combining static passwords and

one-time passwords (TOTP). Especially when

performing sensitive operations, MFA effectively

prevents the security risks associated with a single

password leakage.

2.3 Implementation Details

In the implementation of the system, this study used

Java environment for development. To improve the

accuracy of the model, this study performed data

enhancement operations and used the TOTP

algorithm to generate one-time passwords. In the

experiments, the following hyper-parameter settings

were used: the depth of static analysis was 3 layers,

the number of inputs for fuzz testing was 1000

random samples, and the timeout for MFA was 60

seconds.

3 RESULT AND DISCUSSION

This chapter will summaries and analyze the

experimental results, mainly discussing the results of

static code analysis, dynamic testing, and fuzz testing,

as well as analyzing the system security enhancement

after the introduction of the multi-signature

mechanism and multi-factor authentication.

3.1 Static Code Analysis Results

As shown in Figure 2, the static code analysis of the

smart contract was conducted using Mythril, and the

results show that the original smart contract has

several security vulnerabilities, mainly including

reentry attacks and integer overflow problems. These

vulnerabilities were detected before the contract went

live, thus avoiding potential financial losses. Figure 2

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392

Figure 2: Distribution of different types of vulnerabilities in static code analysis (Picture credit: Original).

Figure 3: JUnit dynamic test results show (Picture credit: Original).

shows the detection results of different types of

vulnerabilities, with reentry attacks accounting for

40%, integer overflow for 34%, and the rest being

other types of vulnerabilities. With these vulnerability

reports, the code was further modified to significantly

reduce the potential attack surface.

3.2 Dynamic Test Results

Figure 3 shows the results of dynamic testing using

JUnit after fixing the vulnerabilities found in the

static analysis. The test covers all the modules of the

contract and simulates a variety of operating

environments to ensure the stability and correctness

of each module when it is executed independently.

The tests show that the repaired smart contract

performs well under all boundary conditions and

exception-handling situations, and no new

vulnerabilities were found. This result shows that

JUnit automated testing can detect potential problems

introduced by code changes promptly.

3.3 Fuzz Test Results

Fuzz testing was conducted using Echidna, by

generating a large number of random inputs, Echidna

detected 3 boundary case security vulnerabilities out

of 1000 test samples. These vulnerabilities, although

difficult to catch in normal testing, were exposed and

fixed by fuzz testing. This test result verifies the

effectiveness of fuzz testing in identifying potential

boundary vulnerabilities and further improves the

security of smart contracts.

Blockchain Security Analysis with Multi-Factor Authentication and Multi-Signature Mechanisms

393

Table 1: Comparison of security before and after introducing multi-signature and multi-factor authentication.

Metric Before Introduction After Introduction

Number of Securit

Incidents 20 incidents 5 incidents

Authentication Stren

th Mediu

Transaction Ris

High Low

Single Password Attack Ris

High Very Low

Overall System Stabilit

Fai

Significantly Improve

3.4 Effectiveness of Security

Enhancement Measures

After adding multi-signature and multi-factor

authentication, the overall security of the system is

significantly improved. Table 1 shows the security

comparison before and after the introduction of these

mechanisms. After adding multi-signature, important

operations such as fund transfers must be co-signed

by multiple authorized persons, effectively

preventing the risks caused by the failure of a single

signature. Meanwhile, multi-factor authentication

prevents account risks caused by password leakage

through the combination of static and one-time

passwords. Overall, this chapter comprehensively

improves the security of the smart contract system

through static analysis, dynamic testing, fuzz testing,

and the introduction of security mechanisms to ensure

the robustness and security of the system in various

scenarios.

4 CONCLUSIONS

The primary objective of this study is to analyze and

enhance the security of blockchain smart contracts.

The proposed methodology integrates static code

analysis, dynamic testing, and fuzz testing, while also

introducing multi-signature mechanisms and multi-

factor authentication to strengthen system security.

The effectiveness of this approach was evaluated

through extensive experiments. The results

demonstrate that static code analysis effectively

identifies common security vulnerabilities, dynamic

testing ensures code stability during execution, and

fuzz testing uncovers potential vulnerabilities at the

boundaries. Additionally, the incorporation of

security enhancements significantly mitigates the

risks associated with high-risk operations. Future

research will focus on addressing the security

challenges of blockchain systems in diverse

application scenarios, particularly in IoT and

financial sectors, to further refine and improve smart

contract protection mechanisms. Additionally,

exploring the potential impact of quantum computing

on blockchain cryptographic algorithms will be a

crucial area of investigation.

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