Authorship Analysis on Dark Marketplace Forums

Spitters, Martijn; Klaver, Femke; Koot, Gijs; Staalduinen, Mark van

doi:10.1109/eisic.2015.47

Cited by 32 publications

(24 citation statements)

References 25 publications

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“…Consequently, there has also been increasing interest in illicit transactions on the Dark Web [24,31,64,65]. Currently, mainly proprietary or non-easily accessible and implementable techniques for Dark Web marketplace investigations are available [33,46]. Thus, there is an increasing need to make Dark Web investigations more accessible and more practical for scholars and practitioners [2,3,50].…”

Section: Discussionmentioning

confidence: 99%

“…Identifying marketplace owners, and their customers, is still somewhat perplexing [33]. This research has provided empirical evidence of success, which has been derived from our proposed analytical framework ( Figure 1).…”

Section: Introductionmentioning

confidence: 90%

“…Dark Web marketplaces have thousands of product listings and user accounts, and therefore it is virtually impossible for manual investigative and analytical techniques to yield actionable data expeditiously. In fact, identifying marketplace owners, customers and products remains challenging [33]. Therefore, the aim of this research is to provide an analytical framework for automated scraping and analysis that can be more accessible to scholars and practitioners.…”

Section: Introductionmentioning

confidence: 99%

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A Framework for More Effective Dark Web Marketplace Investigations

2018

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Abstract:The success of the Silk Road has prompted the growth of many Dark Web marketplaces. This exponential growth has provided criminal enterprises with new outlets to sell illicit items. Thus, the Dark Web has generated great interest from academics and governments who have sought to unveil the identities of participants in these highly lucrative, yet illegal, marketplaces. Traditional Web scraping methodologies and investigative techniques have proven to be inept at unmasking these marketplace participants. This research provides an analytical framework for automating Dark Web scraping and analysis with free tools found on the World Wide Web. Using a case study marketplace, we successfully tested a Web crawler, developed using AppleScript, to retrieve the account information for thousands of vendors and their respective marketplace listings. This paper clearly details why AppleScript was the most viable and efficient method for scraping Dark Web marketplaces. The results from our case study validate the efficacy of our proposed analytical framework, which has relevance for academics studying this growing phenomenon and for investigators examining criminal activity on the Dark Web.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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A Framework for More Effective Dark Web Marketplace Investigations

2018

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“…The work that is perhaps the closest in spirit to the results that we show here is [29], in which the authors tackle the problems of determining aliases and attributing authorship using machine learning classifiers that work with three kinds of features: character-level n-grams, stylometry, and timestamps. Though their approach to determining aliases shares several aspects with our own, the main differences lie in that their solutions involve a high degree of specialization, since single topic posts are used in the evaluation, stylometry-based techniques that depend on the specific data, and analysis of timestamps-on other other hand, our goal is to develop a general approach that (i) requires minimal domain-specific preparation; (ii) is flexible with respect to the set of users in the domain (i.e., it should be easy to add or remove users); (iii) makes as few assumptions as possible regarding user behavior.…”

Section: Related Workmentioning

confidence: 99%

First Steps towards Data-driven Adversarial Deduplication

Paredes¹,

Simari²,

Martínez³

et al. 2018

Preprint

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Abstract:In traditional databases, the entity resolution problem (which is also known as deduplication), refers to the task of mapping multiple manifestations of virtual objects to its corresponding real-world entity. When addressing this problem, in both theory and practice, it is widely assumed that such sets of virtual objects appear as the result of clerical errors, transliterations, missing or updated attributes, abbreviations, and so forth. In this paper, we address this problem under the assumption that this situation is caused by malicious actors operating in domains in which they do not wish to be identified, such as hacker forums and markets in which the participants are motivated to remain semi-anonymous (though they wish to keep their true identities secret, they find it useful for customers to identify their products and services). We are therefore in the presence of a different, even more challenging problem that we refer to as adversarial deduplication. In this paper, we study this problem via examples that arise from real-world data on malicious hacker forums and markets arising from collaborations with a cyber threat intelligence company focusing on understanding this kind of behavior. We argue that it is very difficult-if not impossible-to find ground truth data on which to build solutions to this problem, and develop a set of preliminary experiments based on training machine learning classifiers that leverage text analysis to detect potential cases of duplicate entities. Our results are encouraging as a first step towards building tools that human analysts can use to enhance their capabilities towards fighting cyber threats.

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“…Most research done are focusing in identifying extremist views and identifying extremist groups. Only recently there interest has started to surge in others areas, including drug trafficking [1], looking at alias classification and authorship attribution. In this paper the focus will be on the analysis of the Dark Web marketplaces as a phenomenon and on an in-depth analysis of Agora, a marketplace for drugs, counterfeit documents and ids that we monitored for our research.…”

Section: Introductionmentioning

confidence: 99%

Mining the Dark Web: Drugs and Fake Ids

Baravalle

Lopez

Lee

2016

2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)

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-In the last years, governmental bodies have been futilely trying to fight against dark web marketplaces. Shortly after the closing of "The Silk Road" by the FBI and Europol in 2013, new successors have been established. Through the combination of cryptocurrencies and nonstandard communication protocols and tools, agents can anonymously trade in a marketplace for illegal items without leaving any record. This paper presents a research carried out to gain insights on the products and services sold within one of the larger marketplaces for drugs, fake ids and weapons on the Internet, Agora. Our work sheds a light on the nature of the market; there is a clear preponderance of drugs, which accounts for nearly 80% of the total items on sale. The ready availability of counterfeit documents, while they make up for a much smaller percentage of the market, raises worries. Finally, the role of organized crime within Agora is discussed and presented.

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Authorship Analysis on Dark Marketplace Forums

Cited by 32 publications

References 25 publications

A Framework for More Effective Dark Web Marketplace Investigations

A Framework for More Effective Dark Web Marketplace Investigations

First Steps towards Data-driven Adversarial Deduplication

Mining the Dark Web: Drugs and Fake Ids

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