Cybercriminal Minds: An investigative study of cryptocurrency abuses in the Dark Web

  title={Cybercriminal Minds: An investigative study of cryptocurrency abuses in the Dark Web},
  author={Seunghyeon Lee and Changhoon Yoon and Heedo Kang and Yeonkeun Kim and Yongdae Kim and Dongsu Han and Sooel Son and Seungwon Shin},
  journal={Proceedings 2019 Network and Distributed System Security Symposium},
The Dark Web is notorious for being a major distribution channel of harmful content as well as unlawful goods. [] Key Method Specifically, MFScope collected more than 27 million dark webpages and extracted around 10 million unique cryptocurrency addresses for Bitcoin, Ethereum, and Monero. It then classified their usages to identify trades of illicit goods and traced cryptocurrency money flows, to reveal black money operations on the Dark Web. In total, using MFScope we discovered that more than 80% of…

Dark Web in the Dark: Investigating when Transactions Take Place on Cryptomarkets

This study examines the impact of a cryptomarket policing effort known as Operation Onymous, and indicates that this policing effort only displaced users among these marketplaces and did not deter their activity, even in the short-term.

Quantifying Dark Web Shops’ Illicit Revenue

This paper proposes a methodology to estimate both size and nature of illicit commercial activity on the Dark Web, and demonstrates this based on crawling Tor for single-vendor Dark Web Shops, i.e., niche storefronts operated by single cybercriminal actors or small groups.

Dynamics of Dark Web Financial Marketplaces: An Exploratory Study of Underground Fraud and Scam Business

The findings suggest that the Dark Web financial market is likely to harbor scams targeting Dark Web buyers, and Dark Web sellers construct a website to sell scam products and recommend purchasing Escrow services to ensure safe transactions as an additional scam.

A Survey of the Dark Web and Dark Market Research

  • Hengrui ZhangFutai Zou
  • Computer Science
    2020 IEEE 6th International Conference on Computer and Communications (ICCC)
  • 2020
This article analyzes and summarizes the current status of Dark Web communication technology, Dark Web research methods and research status, and discusses the challenges of Dark web re-search.

Measuring dark web marketplaces via Bitcoin transactions: From birth to independence

This study measures the evolution of the anonymous marketplaces Silk Road, Silk Road 2.0, Agora, Evolution, Nucleus, Abraxas, and AlphaBay, which were the seven leading and most active dark web marketplaces to provide evidence on market size, development, and fluctuation over time to fill a gap in previous studies.

Characterizing Cryptocurrency-themed Malicious Browser Extensions

This work conducts the first systematic study to identify and characterize cryptocurrency-themed malicious extensions and serves as a warning to extension users, and an appeal to extension store operators to enact dedicated countermeasures.

SoK: Cryptojacking Malware

A systematic overview of cryptojacking malware is presented based on the information obtained from the combination of academic research papers, two large Cryptojacking datasets of samples, and 45 major attack instances to help the research community in this emerging area.

Studying Bitcoin Privacy Attacks and Their Impact on Bitcoin-Based Identity Methods

This paper reviews and categorizes Bitcoin privacy attacks, investigates their impact on one of the Bitcoin-based identity methods namely did:btcr, and analyzes and discusses its privacy properties.

A Market in Dream: the Rapid Development of Anonymous Cybercrime

In this paper we have conducted a comprehensive measurement and analysis on the Dream market, an anonymous online market that uses cryptocurrency as transaction currency. We first collect data

A Market in Dream: the Rapid Development of Anonymous Cybercrime

In this paper we have conducted a comprehensive measurement and analysis on the Dream market, an anonymous online market that uses cryptocurrency as transaction currency. We first collect data



An inquiry into money laundering tools in the Bitcoin ecosystem

A first systematic account of opportunities and limitations of anti-money laundering (AML) in Bitcoin, a decentralized cryptographic currency proliferating on the Internet, is provided and it appears unlikely that a Know-Your-Customer principle can be enforced in the Bitcoin system.

Sex, Drugs, and Bitcoin: How Much Illegal Activity Is Financed Through Cryptocurrencies?

It is estimated that around $76 billion of illegal activity per year involves bitcoin (46% of bitcoin transactions), which is close to the scale of the US and European markets for illegal drugs.

Ransomware Payments in the Bitcoin Ecosystem

A data-driven method for identifying and gathering information on Bitcoin transactions related to illicit activity based on footprints left on the public Bitcoin blockchain is presented and found that the market is highly skewed with only a few number of players responsible for the majority of the payments.

Anonymity of Bitcoin Transactions An Analysis of Mixing Services

This paper evaluates three special bitcoin mixing services – Bitcoin Fog, BitLaundry, and the Send Shared functionality of – by analyzing the transaction graph and is able to find a direct relation between the input and output transactions in the graph of Bit laundry.

Data Mining for Detecting Bitcoin Ponzi Schemes

A dataset of features of real-world Ponzi schemes, constructed by analysing, on the Bitcoin blockchain, the transactions used to perform the scams, is used to experiment with various machine learning algorithms, and their effectiveness is assessed through standard validation protocols and performance metrics.

CoinShuffle: Practical Decentralized Coin Mixing for Bitcoin

CoinShuffle is a completely decentralized Bitcoin mixing protocol that allows users to utilize Bitcoin in a truly anonymous manner and it does not require any trusted, accountable or untrusted third party and it is perfectly compatible with the current Bitcoin system.

Backpage and Bitcoin: Uncovering Human Traffickers

A machine learning classifier that uses stylometry to distinguish between ads posted by the same vs. different authors with 90% TPR and 1% FPR is developed and a linking technique that takes advantage of leakages from the Bitcoin mempool, blockchain and sex ad site, to link a subset of sex ads to Bitcoin public wallets and transactions is designed.

There's No Free Lunch, Even Using Bitcoin: Tracking the Popularity and Profits of Virtual Currency Scams

The first empirical analysis of Bitcoin-based scams: operations established with fraudulent intent is presented, finding that at least $11 million has been contributed to the scams from 13 000 distinct victims.

Automatic Bitcoin Address Clustering

This paper proposes to use off-chain information as votes for address separation and to consider it together with blockchain information during the clustering model construction step, showing the feasibility of a proposed approached for Bitcoin address clustering.