Covertly Probing Underground Economy Marketplaces

Fallmann, Hanno; Wondracek, Gilbert; Platzer, Christian

doi:10.1007/978-3-642-14215-4_6

Cited by 31 publications

(28 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Custom crawlers in the literature either lack technical solutions for the challenges posed by Fu et al in 2010, or they do not clearly explain how they deal with them, for example, when dealing with login functionality [12] or with CAPTCHA challenges that prevent Track: Industry WWW 2018, April 23-27, 2018, Lyon, France auto-login [22]. Others opt to exclude forums that require registration [43].…”

Section: Related Workmentioning

confidence: 99%

CrimeBB

Pastrana

Thomas

Hutchings

et al. 2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

View full text Add to dashboard Cite

Underground forums allow criminals to interact, exchange knowledge, and trade in products and services. They also provide a pathway into cybercrime, tempting the curious to join those already motivated to obtain easy money. Analysing these forums enables us to better understand the behaviours of offenders and pathways into crime. Prior research has been valuable, but limited by a reliance on datasets that are incomplete or outdated. More complete data, going back many years, allows for comprehensive research into the evolution of forums and their users. We describe CrimeBot, a crawler designed around the particular challenges of capturing data from underground forums. CrimeBot is used to update and maintain CrimeBB, a dataset of more than 48m posts made from 1m accounts in 4 different operational forums over a decade. This dataset presents a new opportunity for large-scale and longitudinal analysis using up-to-date information. We illustrate the potential by presenting a case study using CrimeBB, which analyses which activities lead new actors into engagement with cybercrime. CrimeBB is available to other academic researchers under a legal agreement, designed to prevent misuse and provide safeguards for ethical research. CCS CONCEPTS• Information systems → Web crawling; • Security and privacy → Social aspects of security and privacy;

show abstract

Section: Related Workmentioning

confidence: 99%

CrimeBB

Pastrana

Thomas

Hutchings

et al. 2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

View full text Add to dashboard Cite

show abstract

“…1 Over time, many of these markets moved to using persistent Web forums and expanded to cover a broader range of information sharing. Zhuge et al first documented the use of such forums in China [12] and contemporary analyses have been published by Holt et al [5], Radianti [7] and Fallmann et al [3]. Over time, some of these forums have specialized and many have moved to "closed" models (i.e., in which new members must be explicitly vouched for by existing members); for example, StoneGross et al [9] recently documented the membership and goods on offer on the private Spamdot.biz forum, which specialized in support for email spammers.…”

Section: Introductionmentioning

confidence: 99%

An analysis of underground forums

Motoyama

McCoy

Levchenko

et al. 2011

Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference

241

206

View full text Add to dashboard Cite

Underground forums, where participants exchange information on abusive tactics and engage in the sale of illegal goods and services, are a form of online social network (OSN). However, unlike traditional OSNs such as Facebook, in underground forums the pattern of communications does not simply encode pre-existing social relationships, but instead captures the dynamic trust relationships forged between mutually distrustful parties. In this paper, we empirically characterize six different underground forumsBlackHatWorld, Carders, HackSector, HackE1ite, Freehack, and L33tCrew -examining the properties of the social networks formed within, the content of the goods and services being exchanged, and lastly, how individuals gain and lose trust in this setting.

show abstract

“…A major reason for this is that in most cases credit card numbers are traded, which leads to credit card frauds where money is stolen from the victim's bank account. Our classification system can be used alongside existing systems for monitoring the underground economy, such as [2]. As any real-world implementation of such a system will have limited computing and networking resources, it is clearly beneficial to be able to automatically focus on monitoring interesting channels.…”

Section: Collecting Datamentioning

confidence: 99%

“…Furthermore, we also compared different vector space models and evaluated the effectiveness of the document selection. Our data corpus was collected over a period of eleven months via an observation framework [2]. During this period, we managed to capture 51.3 million IRC messages transmitted over 2,693 channels on 246 networks.…”

Section: Training Processmentioning

confidence: 99%

See 1 more Smart Citation

Automated Analysis of Underground Marketplaces

Hudic

Krombholz

Otterbein

et al. 2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Self Cite

View full text Add to dashboard Cite

Cyber crime, such as theft of credentials or credit card fraud has emerged as a new type of crime in recent years. Cyber criminals usually attack Internet services to steal sensitive data and operate in crowded online underground marketplaces.Crime investigators and digital forensics are trying to detect and analyze these marketplaces. However, due to the lack of efficient and reliable methods to detect underground marketplaces, investigators have to analyze those channels manually. This is a complex and time-consuming task that is associated with high financial costs. In this work, we demonstrate how machine-learning algorithms can be efficiently used to automatically determine whether a communication channel is used as an underground marketplace. Our approach includes specific design features related to the context domain of cyber crime and can be used to reliably detect and observe marketplaces of the underground economy. The manual effort is significantly reduced, leading to lower financial costs, less time required and higher efficiency. We implemented a prototype that classified 51,3 million message samples correctly which implicates that machine learning can be efficiently used for a forensic analysis of underground marketplaces.

show abstract

Covertly Probing Underground Economy Marketplaces

Cited by 31 publications

References 7 publications

CrimeBB

CrimeBB

An analysis of underground forums

Automated Analysis of Underground Marketplaces

Contact Info

Product

Resources

About