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Characterizing Cryptocurrency Exchange Scams

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TLDR
This paper identifies over 1,500 scam domains and over 300 fake apps, by collecting existing reports and using typosquatting generation techniques, and characterize the impacts of such scams, revealing that they have incurred financial loss of 520k US dollars at least.
Abstract: 
As the indispensable trading platforms of the ecosystem, hundreds of cryptocurrency exchanges are emerging to facilitate the trading of digital assets. While, it also attracts the attentions of attackers. A number of scam attacks were reported targeting cryptocurrency exchanges, leading to a huge mount of financial loss. However, no previous work in our research community has systematically studied this problem. In this paper, we make the first effort to identify and characterize the cryptocurrency exchange scams. We first identify over 1,500 scam domains and over 300 fake apps, by collecting existing reports and using typosquatting generation techniques. Then we investigate the relationship between them, and identify 94 scam domain families and 30 fake app families. We further characterize the impacts of such scams, and reveal that these scams have incurred financial loss of 520k US dollars at least. We further observe that the fake apps have been sneaked to major app markets (including Google Play) to infect unsuspicious users. Our findings demonstrate the urgency to identify and prevent cryptocurrency exchange scams. To facilitate future research, we have publicly released all the identified scam domains and fake apps to the community.

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Citations
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References
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Book ChapterDOI

A Survey of Attacks on Ethereum Smart Contracts SoK

TL;DR: This work analyses the security vulnerabilities of Ethereum smart contracts, providing a taxonomy of common programming pitfalls which may lead to vulnerabilities, and shows a series of attacks which exploit these vulnerabilities, allowing an adversary to steal money or cause other damage.
Proceedings ArticleDOI

Detecting Ponzi Schemes on Ethereum: Towards Healthier Blockchain Technology

TL;DR: By verifying smart contracts on Ethereum, this paper first extracts features from user accounts and operation codes of the smart contracts and then builds a classification model to detect latent Ponzi schemes implemented as smart contracts, and shows that the proposed approach can achieve high accuracy for practical use.
Proceedings ArticleDOI

Understanding Ethereum via Graph Analysis

TL;DR: This paper designs a new approach to collect all transaction data, constructs three graphs from the data to characterize major activities on Ethereum, and proposes new approaches based on cross-graph analysis to address two security issues in Ethereum.
Proceedings ArticleDOI

Data Mining for Detecting Bitcoin Ponzi Schemes

TL;DR: In this article, the authors apply data mining techniques to detect Bitcoin addresses related to Ponzi schemes, which are fraudulent investments which repay users with the funds invested by new users that join the scheme, and implode when it is no longer possible to find new investments.
Journal ArticleDOI

Dissecting Ponzi schemes on Ethereum: Identification, analysis, and impact

TL;DR: A comprehensive survey of Ponzi schemes on Ethereum, analysing their behaviour and their impact from various viewpoints shows that they still make users lose money, but at least are guaranteed to execute "correctly".
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