Understanding eWhoring

Hutchings, Alice; Pastrana, Sergio

doi:10.1109/eurosp.2019.00024

Cited by 19 publications

(26 citation statements)

References 30 publications

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“…The heuristics are formed through our expertise in analysing forum data [19,25,27]. Concretely, for each thread we account for keywords frequently observed in TOP headings such as 'images', 'video' or 'unsaturated'.…”

Section: Extracting Threads Offering Packsmentioning

confidence: 99%

“…However, until recently the topic remained largely hidden from academic attention. Earlier work [19] provides a qualitative understanding of eWhoring, exploring how it works, how the actors monetise their activities, and possible intervention approaches. This qualitative approach allows us to understand what eWhoring involves and informs the types of measurements we present here.…”

Section: Introductionmentioning

confidence: 99%

“…Previous research into eWhoring used a crime scripting approach, analysing the tutorials and discussions posted on the underground forum hackforums.net. These tutorials provide instructions about how to get involved in eWhoring, and how to monetise this activity [19]. The crime script describes the steps involved in eWhoring, from preparation to exchanging the illicitly obtained funds.…”

Section: Introductionmentioning

confidence: 99%

“…Our previous work used content analysis methods to generate the eWhoring crime script [19]. This qualitative approach provided useful information about the processes involved in eWhoring, and how this fraud is monetised.…”

Section: Introductionmentioning

confidence: 99%

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Measuring eWhoring

Pastrana

Hutchings

Thomas

et al. 2019

Proceedings of the Internet Measurement Conference

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eWhoring is the term used by offenders to refer to a type of online fraud in which cybersexual encounters are simulated for financial gain. Perpetrators use social engineering techniques to impersonate young women in online communities, e.g., chat or social networking sites. They engage potential customers in conversation with the aim of selling misleading sexual material-mostly photographs and interactive video shows-illicitly compiled from third-party sites. eWhoring is a popular topic in underground communities, with forums acting as a gateway into offending. Users not only share knowledge and tutorials, but also trade in goods and services, such as packs of images and videos. In this paper, we present a processing pipeline to quantitatively analyse various aspects of eWhoring. Our pipeline integrates multiple tools to crawl, annotate, and classify material in a semi-automatic way. It builds in precautions to safeguard against significant ethical issues, such as avoiding the researchers' exposure to pornographic material, and legal concerns, which were justified as some of the images were classified as child exploitation material. We use it to perform a longitudinal measurement of eWhoring activities in 10 specialised underground forums from 2008 to 2019. Our study focuses on three of the main eWhoring components: (i) the acquisition and provenance of images; (ii) the financial profits and monetisation techniques; and (iii) a social network analysis of the offenders, including their relationships, interests, and pathways before and after engaging in this fraudulent activity. We provide recommendations, including potential intervention approaches. CCS CONCEPTS • Security and privacy → Social engineering attacks; Social network security and privacy; • Social and professional topics → Financial crime.

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Section: Extracting Threads Offering Packsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Measuring eWhoring

Pastrana

Hutchings

Thomas

et al. 2019

Proceedings of the Internet Measurement Conference

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“…Fraud detection is a sparse subject relevant to many domains: credit card fraud [10,17], tax evasion [7], online dating [46], erotic content [23], advertising [36], among others. Despite efforts to systemize it [8,37,43], there is no commonly accepted means of comparing different detection techniques or application scenarios.…”

Section: Fraud Detectionmentioning

confidence: 99%

Detecting organized eCommerce fraud using scalable categorical clustering

Marchal

Szyller

2019

Proceedings of the 35th Annual Computer Security Applications Conference

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Online retail, eCommerce, frequently falls victim to fraud conducted by malicious customers (fraudsters) who obtain goods or services through deception. Fraud coordinated by groups of professional fraudsters that place several fraudulent orders to maximize their gain is referred to as organized fraud. Existing approaches to fraud detection typically analyze orders in isolation and they are not effective at identifying groups of fraudulent orders linked to organized fraud. These also wrongly identify many legitimate orders as fraud, which hinders their usage for automated fraud cancellation. We introduce a novel solution to detect organized fraud by analyzing orders in bulk. Our approach is based on clustering and aims to group together fraudulent orders placed by the same group of fraudsters. It selectively uses two existing techniques, agglomerative clustering and sampling to recursively group orders into small clusters in a reasonable amount of time. We assess our clustering technique on real-world orders placed on the Zalando website, the largest online apparel retailer in Europe 1 . Our clustering processes 100,000s of orders in a few hours and groups 35-45% of fraudulent orders together. We propose a simple technique built on top of our clustering that detects 26.2% of fraud while raising false alarms for only 0.1% of legitimate orders.

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