Cryptocurrency Wallets: Assessment and Security

Nowroozi, Ehsan; Seyedshoari, Seyedsadra; Mekdad, Yassine; Savaş, Erkay; Conti, Mauro

doi:10.1007/978-3-031-25506-9_1

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Layering is another technique, involving moving funds through multiple transactions and accounts to obscure the original source (Mooij, 2023). Criminals may also use multiple wallets and exchanges to transfer funds, making it challenging for authorities to track the flow of money (Nowroozi et al, 2022). Peerto-peer (P2P) exchanges also allow users to trade cryptocurrencies directly with one another, without a centralized exchange.…”

Section: Introductionmentioning

confidence: 99%

Integrating Machine Learning in Anti-Money Laundering through Crypto: A Comprehensive Performance Review

Japinye

2024

EJAAFR

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The integration of machine learning (ML) algorithms in Anti-Money Laundering (AML) practices has garnered significant attention due to its potential to enhance the detection and prevention of illicit activities in the cryptocurrency ecosystem. This systematic literature review analysed the effectiveness of integrating ML algorithms in detecting and preventing crypto laundering activities, identify the most frequently used ML algorithms, examine trends in publication and research methodologies, and discuss key challenges and constraints associated with integrating ML technologies into AML frameworks. A comprehensive search strategy was employed to identify relevant studies, resulting in the inclusion of 52 articles published between 2019 and 2023. The findings reveal a growing interest in the field, with a notable increase in publications in recent years. Traditional ML models such as Logistic Regression, Random Forest, and Support Vector Machine (SVM) remain prevalent, while deep learning models like Multilayer Perceptrons (MLP) and Long Short-Term Memory (LSTM) networks are gaining popularity. Graph Convolutional Networks (GCNs) have emerged as a significant area of exploration, particularly in the context of graph data analysis in cryptocurrencies. Despite advancements in ML, cryptocurrencies continue to pose a high risk of money laundering due to the practical challenge of implementation ownership of the various ML models. Future research should focus on how these challenges will be addressed to ensure the effective and sustainable use of ML technologies in real-world AML practices.

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Section: Introductionmentioning

confidence: 99%

Integrating Machine Learning in Anti-Money Laundering through Crypto: A Comprehensive Performance Review

Japinye

2024

EJAAFR

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A Security Analysis of Cryptocurrency Wallets against Password Brute-Force Attacks

Byun,

Kim,

Jeong

et al. 2024

Electronics

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Currently, the monetary value of cryptocurrencies is extremely high, leading to frequent theft attempts. Cyberattacks targeting cryptocurrency wallets and the scale of these attacks are also increasing annually. However, many studies focus on large-scale exchanges, leading to a lack of research on cryptocurrency wallet security. Nevertheless, the threat to individual wallets is real and can lead to severe consequences for individuals. In this paper, we analyze the security of the open-source cryptocurrency wallets Sparrow, Etherwall, and Bither against brute-force attacks, a fundamental threat in password-based systems. As cryptocurrency wallets use passwords to manage users’ private keys, we analyzed the private key management mechanism and implemented a password verification oracle. We used this oracle for brute-force attacks. We identified the private key management mechanism by conducting a code-level investigation and evaluated the three wallets’ security through practical experimentation. The experiment results revealed that the wallets’ security, which depends on passwords, could be diminished due to the password input space and the configuration of password length settings. We propose a general methodology for analyzing the security of desktop cryptocurrency wallets against brute-force attacks and provide practical guidelines for designing secure wallets. By using the analysis methods suggested in this paper, one can evaluate the security of wallets.

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Cryptocurrency Wallets: Assessment and Security

Cited by 2 publications

References 20 publications

Integrating Machine Learning in Anti-Money Laundering through Crypto: A Comprehensive Performance Review

Integrating Machine Learning in Anti-Money Laundering through Crypto: A Comprehensive Performance Review

A Security Analysis of Cryptocurrency Wallets against Password Brute-Force Attacks

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