Risk Avoidance Behavior on Darknet Marketplaces

Howell, C. Jordan; Maimon, David; Perkins, Robert C.; Burruss, George W.; Ouellet, Marie; Wu, Yubao

doi:10.1177/00111287221092713

Cited by 15 publications

(29 citation statements)

References 42 publications

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“…Finally, although we are far from the first to examine illegal transactions on darknet markets (e.g., Howell et al, 2022; Ouellet et al, 2022), our article adds to the literature by examining these markets using novel and unique data. We are able to expand the understanding of criminologists by specifically considering heuristics within financial transactions.…”

Section: Discussionmentioning

confidence: 99%

“…Although little criminological research has been conducted on darknet markets, what has been done typically follows a proactive, policy-oriented approach. This largely includes the potential disruption of illegal marketplaces, for example, “lemonising the market” (Hoe et al, 2012; Howell et al, 2022; Hutchings & Holt, 2017), fake sales, such as “fake peach attack” (Hoe et al, 2012), and utilizing additional scripts to introduce new information into illegal markets (Hutchings & Holt, 2015; Jardine, 2021). Generally speaking, these methods involve introducing new information into criminal markets and are designed to erode trust.…”

Section: Literature Reviewmentioning

confidence: 99%

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Follow the Money: Analyzing Darknet Activity Using Cryptocurrency and the Bitcoin Blockchain

Dearden

Tucker

2023

Journal of Contemporary Criminal Justice

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Transactions on the darknet are notoriously difficult to examine. Prior criminological research has generally used web scraping and qualitative text analysis to examine illegal darknet markets. One disadvantage of this process is that individuals can lie. Fortunately for researchers, the currency used for transactions on the darknet, cryptocurrency, is designed to be tracked. In this article, we examine transactions from a former darknet marketplace, AlphaBay. Using the blockchain, we examine the interconnectedness of both legal and illegal cryptocurrencies. In addition, we provide a structured approach to quantitatively examine the Bitcoin blockchain ledger, offering both the tools and our own experiences for other researchers interested in such approaches. While cybersecurity, information technology, accounting, and other disciplines can examine the financial data itself, we believe that criminologists can provide additional benefits in pattern analysis and organizing the context and theory around the transactions. Our results show that cryptocurrency transactions are generally identifiable (90%) and involve likely illegal transactions, transactions that attempt to obfuscate other transactions, and legal transactions. We end with a discussion of newer cryptocurrencies and related technology and how they will likely shape future work.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Follow the Money: Analyzing Darknet Activity Using Cryptocurrency and the Bitcoin Blockchain

Dearden

Tucker

2023

Journal of Contemporary Criminal Justice

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“…Ross William Ulbricht (Paul, 2018) The DW, known as "Dread Pirate Roberts (Howell et al, 2022)," created the most well-known e-market market (Silk Road) and trades in drugs, malware, hacking services, money laundering, account forgery, stolen social and credit cards, multimedia pirated tools, forged passports, and social card ID (Heinl et al, 2019). In September 2013, the FBI found and shut down the website (Ulbricht was arrested in October) (Alshammery & Aljuboori, 2022).…”

Section: Layers Of the Internetmentioning

confidence: 99%

Perceptual Operating Systems for the Trade Associations of Cyber Criminals to Scrutinize Hazardous Content

Rawat,

Rajavat

2024

International Journal of Cyber Warfare and Terrorism

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The limits of user visibility have been exceeded by the internet. The “Dark Web” or “Dark Net” refers to certain unknown portions of the internet that cannot be found using standard search methods. A number of computerised techniques are being explored to extract or crawl the concealed data. All users can freely interact on the surface web. Identity identities may be found on the deep web, and the dark web (DW), a hub for anonymous data, is a haven for terrorists and cybercriminals to promote their ideologies and illegal activities. Officials in clandestine surveillance and cyberpolicing are always trying to track down offenders' trails or hints. The search for DW offenders might take five to ten years.The proposed study provides data from a DW mining and online marketplaces situation from a few domains, as well as an overview for investigators to build an automated engine for scraping all dangerous information from related sites.

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“…Such actions, or even gossip surrounding exit scams, can be detrimental to the market’s continued success. Indeed, Howell et al (2022) found that gossip delivered from a credible source reduced the number of transactions made, and amount of revenue generated, on the market. Importantly, Howell et al (2022) recognized that gossip may have displaced, rather than deterred, market participants and suggested that future studies assess the ecosystem rather than single markets.…”

Section: Literature Reviewmentioning

confidence: 99%

A Depiction and Classification of the Stolen Data Market Ecosystem and Comprising Darknet Markets: A Multidisciplinary Approach

Howell

Fisher

Muniz

et al. 2023

Journal of Contemporary Criminal Justice

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Scant research has investigated the illicit online ecosystem that enables the sale of stolen data. Even fewer studies have examined the longitudinal trends of the markets on which these data are bought and sold. To fill this gap in the literature, our research team identified 30 darknet markets advertising stolen data products from September 1, 2020, through April 30, 2021. We then developed python web scrapers to systematically extract information pertaining to stolen data products on a weekly basis. Using these data, we calculated the number of vendors, listings, sales, and revenue across the markets and at the aggregate, ecosystem level. Moreover, we developed a data-driven market classification system drawing from ecological principles and dominant firm theory. Findings indicate that markets vary in size and success. Although some markets generated over $91 million in revenue from stolen data products, the median revenue across markets during the observational period was only $95,509. Variability also exists across markets in respect to the number of vendors, listings, and sales. Only three markets were classified as financially successful and stable (i.e., dominant firms). We argue resources should be allocated to target markets fitting these criteria.

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Risk Avoidance Behavior on Darknet Marketplaces

Cited by 15 publications

References 42 publications

Follow the Money: Analyzing Darknet Activity Using Cryptocurrency and the Bitcoin Blockchain

Follow the Money: Analyzing Darknet Activity Using Cryptocurrency and the Bitcoin Blockchain

Perceptual Operating Systems for the Trade Associations of Cyber Criminals to Scrutinize Hazardous Content

A Depiction and Classification of the Stolen Data Market Ecosystem and Comprising Darknet Markets: A Multidisciplinary Approach

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