Underground online forums are platforms that enable trades of illicit services and stolen goods. Carding forums, in particular, are known for being focused on trading financial information. However, little evidence exists about the sellers that are present on carding forums, the precise types of products they advertise, and the prices buyers pay. Existing literature mainly focuses on the organisation and structure of the forums. Furthermore, studies on carding forums are usually based on literature review, expert interviews, or data from forums that have already been shut down. This paper provides first-of-its-kind empirical evidence on active forums where stolen financial data is traded. We monitored 5 out of 25 discovered forums, collected posts from the forums over a three-month period, and analysed them quantitatively and qualitatively. We focused our analyses on products, prices, seller prolificacy, seller specialisation, and seller reputation.
Generally, there are several types of participants on the forums: sellers, buyers, intermediaries, mules, administrators, and others. These roles are not mutually exclusive; sellers may simultaneously be buyers. Although the total number of participants is in unclear, @cite_27 argue that, based on expert interviews and literature review, the total number of participants on the forums is likely to rise. The increasing spread of different marketplaces and forums would facilitate access to one of them. At least from a historic perspective, Christin @cite_6 confirmed this growth of participants on underground platforms as he observed a linear increase of sellers during his half-year analysis of Silk Road, a large underground marketplace. In the aftermath of Silk Road's take-down in 2013, the number of sellers on competitor - and newcomer-marketplaces has substantially increased, surpassing the original number of sellers on Silk Road @cite_7 .
@cite_27 @cite_7 @cite_6
2269520727 2952768726 2153532176 2122551442 2770149261 2119505155 1982235297 2109816759 2096233795 2004621612 1982381099 1978881802 2519284426 72733388 1994473607 2132962756 2620184808 1559760877 1577841485 1994966918
February 2011 saw the emergence of Silk Road, the first successful online anonymous marketplace, in which buyers and sellers could transact with anonymity properties far superior to those available in alternative online or offline means of commerce. Business on Silk Road, primarily involving narcotics trafficking, rapidly boomed, and competitors emerged. At the same time, law enforcement did not sit idle, and eventually managed to shut down Silk Road in October 2013 and arrest its operator. Far from causing the demise of this novel form of commerce, the Silk Road take-down spawned an entire, dynamic, online anonymous marketplace ecosystem, which has continued to evolve to this day. This paper presents a long-term measurement analysis of a large portion of this online anonymous marketplace ecosystem, including 16 different marketplaces, over more than two years (2013- 2015). By using long-term measurements, and combining our own data collection with publicly available previous efforts, we offer a detailed understanding of the growth of the online anonymous marketplace ecosystem. We are able to document the evolution of the types of goods being sold, and assess the effect (or lack thereof) of adversarial events, such as law enforcement operations or large-scale frauds, on the overall size of the economy. We also provide insights into how vendors are diversifying and replicating across marketplaces, and how vendor security practices (e.g., PGP adoption) are evolving. These different aspects help us understand how traditional, physical-world criminal activities are developing an online presence, in the same manner traditional commerce diversified online in the 1990s. We perform a comprehensive measurement analysis of Silk Road, an anonymous, international online marketplace that operates as a Tor hidden service and uses Bitcoin as its exchange currency. We gather and analyze data over eight months between the end of 2011 and 2012, including daily crawls of the marketplace for nearly six months in 2012. We obtain a detailed picture of the type of goods being sold on Silk Road, and of the revenues made both by sellers and Silk Road operators. Through examining over 24,400 separate items sold on the site, we show that Silk Road is overwhelmingly used as a market for controlled substances and narcotics, and that most items sold are available for less than three weeks. The majority of sellers disappears within roughly three months of their arrival, but a core of 112 sellers has been present throughout our measurement interval. We evaluate the total revenue made by all sellers, from public listings, to slightly over USD 1.2 million per month; this corresponds to about USD 92,000 per month in commissions for the Silk Road operators. We further show that the marketplace has been operating steadily, with daily sales and number of sellers overall increasing over our measurement interval. We discuss economic and policy implications of our analysis and results, including ethical considerations for future research in this area. Over the last decade, the nature of cybercrime has transformed from naive vandalism to profit-driven, leading to the emergence of a global underground economy. A noticeable trend which has surfaced in this economy is the repeated use of forums to operate online stolen data markets. Using interaction data from three prominent carding forums: Shadowcrew, Cardersmarket and Darkmarket, this study sets out to understand why forums are repeatedly chosen to operate online stolen data markets despite numerous successful infiltrations by law enforcement in the past. Drawing on theories from criminology, social psychology, economics and network science, this study has identified four fundamental socio-economic mechanisms offered by carding forums: (1) formal control and coordination; (2) social networking; (3) identity uncertainty mitigation; (4) quality uncertainty mitigation. Together, they give rise to a sophisticated underground market regulatory system that facilitates underground trading over the Internet and thus drives the expansion of the underground economy. 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 forums --- BlackHatWorld, 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. The literature on distributed computing (as well as the cryptography literature) typically considers two types of players—honest players and corrupted players. Resilience properties are then analyzed assuming a lower bound on the fraction of honest players. Honest players, however, are not only assumed to follow the prescribed the protocol, but also assumed to be online throughout the whole execution of the protocol. The advent of “large-scale” consensus protocols (e.g., the blockchain protocol) where we may have millions of players, makes this assumption unrealistic. In this work, we initiate a study of distributed protocols in a “sleepy” model of computation where players can be either online (awake) or offline (asleep), and their online status may change at any point during the protocol. The main question we address is: Can we design consensus protocols that remain resilient under “sporadic participation”, where at any given point, only a subset of the players are actually online? As far as we know, all standard consensus protocols break down under such sporadic participation, even if we assume that (99 ) of the online players are honest. Programming forums are becoming the primary tools for programmers to find answers for their programming problems. Our empirical study of popular programming forums shows that the forum users experience long waiting period for answers and a small number of experts are often overloaded with questions. To improve the usage experience, we have designed and implemented G-Finder, both an algorithm and a tool that makes intelligent routing decisions as to which participant is the expert for answering a particular programming question. Our main approach is to leverage the source code information of the software systems that forums are dedicated to, and discover latent relationships between forums users. Our algorithms construct the concept networks and the user networks from the program source and the forum data.We use programming questions to dynamically integrate these two networks and present an adaptive ranking of the potential experts. Our evaluation of G-Finder, using the data from three large programming forums, takes a retrospective view to check if G-Finder can correctly predict the experts who provided answers to programming questions. The evaluation results show that G-Finder improves the prediction precision by 25 to 74 , compared to related approaches. Online software forums provide a huge amount of valuable content. Developers and users often ask questions and receive answers from such forums. The availability of a vast amount of thread discussions in forums provides ample opportunities for knowledge acquisition and summarization. For a given search query, current search engines use traditional information retrieval approach to extract webpages containing relevant keywords. However, in software forums, often there are many threads containing similar keywords where each thread could contain a lot of posts as many as 1,000 or more. Manually finding relevant answers from these long threads is a painstaking task to the users. Finding relevant answers is particularly hard in software forums as: complexities of software systems cause a huge variety of issues often expressed in similar technical jargons, and software forum users are often expert internet users who often posts answers in multiple venues creating many duplicate posts, often without satisfying answers, in the world wide web. To address this problem, this paper provides a semantic search engine framework to process software threads and recover relevant answers according to user queries. Different from standard information retrieval engine, our framework infer semantic tags of posts in the software forum threads and utilize these tags to recover relevant answer posts. In our case study, we analyze 6,068 posts from three software forums. In terms of accuracy of our inferred tags, we could achieve on average an overall precision, recall and F-measure of 67 , 71 , and 69 respectively. To empirically study the benefit of our overall framework, we also conduct a user-assisted study which shows that as compared to a standard information retrieval approach, our proposed framework could increase mean average precision from 17 to 71 in retrieving relevant answers to various queries and achieve a Normalized Discounted Cumulative Gain (nDCG) @1 score of 91.2 and nDCG@2 score of 71.6 . While social interactions are critical to understanding consumer behavior, the relationship between social and commerce networks has not been explored on a large scale. We analyze Taobao, a Chinese consumer marketplace that is the world's largest e-commerce website. What sets Taobao apart from its competitors is its integrated instant messaging tool, which buyers can use to ask sellers about products or ask other buyers for advice. In our study, we focus on how an individual's commercial transactions are embedded in their social graphs. By studying triads and the directed closure process, we quantify the presence of information passing and gain insights into when different types of links form in the network. Using seller ratings and review information, we then quantify a price of trust. How much will a consumer pay for transaction with a trusted seller? We conclude by modeling this consumer choice problem: if a buyer wishes to purchase a particular product, how does (s)he decide which store to purchase it from? By analyzing the performance of various feature sets in an information retrieval setting, we demonstrate how the social graph factors into understanding consumer behavior. Dozens of markets of all types are in the early stages of a revolution as the Internet and related technologies vastly expand the variety of products that can be produced, promoted and purchased. Although this revolution is based on a simple set of economic and technological drivers, the authors argue that its implications are far-reaching for managers, consumers and the economy as a whole. This article looks at what has been dubbed the "Long Tail" phenomenon, examining how customers derive value from an important characteristic of Internet markets: the ability of online merchants to help consumers locate, evaluate and purchase a far wider range of products than they can typically buy via the traditional brick-and-mortar channels. The article examines the Long Tail from both the supply side and the demand side and identifies several key drivers. On the supply side, the authors point out how e-tailers' expanded, centralized warehousing allows for more offerings, thus making it possible for them to cater to more varied tastes. On the demand side, tools such as search engines, recommender software and sampling tools are allowing customers to find products outside of their geographic area. The authors also look toward the future to discuss second order amplified effects of Long Tail, including the growth of markets serving smaller niches. As consumers are increasingly using the Internet to manage their finances, there has been a concomitant increase in the risk of theft and fraud by cybercriminals. Hackers who acquire sensitive consumer data utilise information on their own, or sell the information in online forums for a significant profit. Few have considered the organisational composition of the participants engaged in the sale of stolen data, including the presence of managerial oversight, division of labour, coordination of roles and purposive associations between buyers, sellers and forum operators. Thus, this qualitative study will apply Best and Luckenbill's framework of social organisation to a sample of threads from publicly accessible web forums where individuals buy and sell stolen financial information. The implications of this study for criminologists, law enforcement, the intelligence community and information security researchers will be discussed in depth. An increasing fraction of the global discourse is migrating online in the form of blogs, bulletin boards, web pages, wikis, editorials, and a dizzying array of new collaborative technologies. The migration has now proceeded to the point that topics reflecting certain individual products are sufficiently popular to allow targeted online tracking of the ebb and flow of chatter around these topics. Based on an analysis of around half a million sales rank values for 2,340 books over a period of four months, and correlating postings in blogs, media, and web pages, we are able to draw several interesting conclusions.First, carefully hand-crafted queries produce matching postings whose volume predicts sales ranks. Second, these queries can be automatically generated in many cases. And third, even though sales rank motion might be difficult to predict in general, algorithmic predictors can use online postings to successfully predict spikes in sales rank. In a wide range of markets, individual buyers and sellers trade through intermediaries, who determine prices via strategic considerations. Typically, not all buyers and sellers have access to the same intermediaries, and they trade at correspondingly different prices that reflect their relative amounts of power in the market. We model this phenomenon using a game in which buyers, sellers, and traders engage in trade on a graph that represents the access each buyer and seller has to the traders. We show that the resulting game always has a subgame perfect Nash equilibrium, and that all equilibria lead to an efficient allocation of goods. Finally, we analyze trader profits in terms of the graph structure -- roughly, a trader can command a positive profit if and only if it has an "essential" connection in the network, thus providing a graph-theoretic basis for quantifying the amount of competition among traders. We study the dynamic mechanism design problem of a seller who repeatedly sells independent items to a buyer with private values. In this setting, the seller could potentially extract the entire buyer surplus by running efficient auctions and charging an upfront participation fee at the beginning of the horizon. In some markets, such as internet advertising, participation fees are not practical since buyers expect to inspect items before purchasing them. This motivates us to study the design of dynamic mechanisms under successively more stringent requirements that capture the implicit business constraints of these markets. We first consider a periodic individual rationality constraint, which limits the mechanism to charge at most the buyer's value in each period. While this prevents large upfront participation fees, the seller can still design mechanisms that spread a participation fee across the first few auctions. These mechanisms have the unappealing feature that they provide close-to-zero buyer utility in earlier auctions in exchange for higher utility in later auctions. To address this problem, we introduce a martingale utility constraint, which imposes the requirement that from the perspective of the buyer, the next item's expected utility is equal to the present one's. Our main result is providing a dynamic auction satisfying martingale utility and periodic individual rationality whose loss in profit with respect to first-best (full extraction of buyer surplus) is optimal up to polylogarithmic factors. The proposed mechanism is a dynamic two-tier auction with a hard floor and a soft floor that allocates the item whenever the buyer's bid is above the hard floor and charges the minimum of the bid and the soft floor. Today, several actors within the Internet's burgeoning underground economy specialize in providing services to like-minded criminals. At the same time, gray and white markets exist for services on the Internet providing reasonably similar products. In this paper we explore a hypothetical arbitrage between these two markets by purchasing "Human Intelligence" on Amazon's Mechanical Turk service, determining the vulnerability of and cost to compromise the computers being used by the humans to provide this service, and estimating the underground value of the computers which are vulnerable to exploitation. We show that it is economically feasible for an attacker to purchase access to high value hosts via Mechanical Turk, compromise the subset with unpatched, vulnerable browser plugins, and sell access to these hosts via Pay-Per-Install programs for a tidy profit. We also present supplementary statistics gathered regarding Mechanical Turk workers' browser security, antivirus usage, and willingness to run arbitrary programs in exchange for a small monetary reward. We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a ‘long tail’ where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies communities, product, and pricing categories for which viral marketing seems to be very effective. Online labor markets, such as Amazon's Mechanical Turk, have been used to crowdsource simple, short tasks like image labeling and transcription. However, expert knowledge is often lacking in such markets, making it impossible to complete certain classes of tasks. In this work we introduce an alternative mechanism for crowdsourcing tasks that require specialized knowledge or skill: communitysourcing --- the use of physical kiosks to elicit work from specific populations. We investigate the potential of communitysourcing by designing, implementing and evaluating Umati: the communitysourcing vending machine. Umati allows users to earn credits by performing tasks using a touchscreen attached to the machine. Physical rewards (in this case, snacks) are dispensed through traditional vending mechanics. We evaluated whether communitysourcing can accomplish expert work by using Umati to grade Computer Science exams. We placed Umati in a university Computer Science building, targeting students with grading tasks for snacks. Over one week, 328 unique users (302 of whom were students) completed 7771 tasks (7240 by students). 80 of users had never participated in a crowdsourcing market before. We found that Umati was able to grade exams with 2 higher accuracy (at the same price) or at 33 lower cost (at equivalent accuracy) than traditional single-expert grading. Mechanical Turk workers had no success grading the same exams. These results indicate that communitysourcing can successfully elicit high-quality expert work from specific communities. How effectively does a decentralized marketplace aggregate information that is dispersed throughout the economy? We study this question in a dynamic setting, in which sellers have private information that is correlated with an unobservable aggregate state. We first characterize equilibria with an arbitrary finite number of informed traders. A common feature is that each seller’s trading behavior provides an informative and conditionally independent signal about the aggregate state. We then ask whether the state is revealed as the number of informed traders goes to infinity. Perhaps surprisingly, the answer is no. We provide generic conditions under which the amount of information revealed is necessarily bounded and does not reveal the aggregate state. When these conditions are violated, we provide conditions under which there is coexistence of aggregating and non-aggregating equilibria. We discuss the implications for policies meant to enhance information dissemination in markets. In general, a partially revealing information policy can increase trading surplus. Cyber-criminals around the world are using Internet-based communication channels to establish trade relationships and complete fraudulent transactions. Furthermore, they control and operate publicly accessible information channels that serve as marketplaces for the underground economy. In this work, we present a novel system for automatically monitoring these channels and their participants. Our approach is focused on creating a stealthy system, which allows it to stay largely undetected by both marketplace operators and participants. We implemented a prototype that is capable of monitoring IRC (Internet Relay Chat) and web forum marketplaces, and successfully performed an experimental evaluation over a period of 11 months. In our experimental evaluation we present the findings about the captured underground information channels and their characteristics. We present the results of a survey that collected information about the demographics of participants on Amazon Mechanical Turk, together with information about their level of activity and motivation for working on Amazon Mechanical Turk. We find that approximately 50 of the workers come from the United States and 40 come from India. Country of origin tends to change the motivating reasons for workers to participate in the marketplace. Significantly more workers from India participate on Mechanical Turk because the online marketplace is a primary source of income, while in the US most workers consider Mechanical Turk a secondary source of income. While money is a primary motivating reason for workers to participate in the marketplace, workers also cite a variety of other motivating reasons, including entertainment and education. As the World Wide Web in China grows rapidly, mining knowledge in Chinese Web pages becomes more and more important. Mining Web information usually relies on the machine learning techniques which require a large amount of labeled data to train credible models. Although the number of Chinese Web pages increases quite fast, it still lacks Chinese labeled data. However, there are relatively sufficient English labeled Web pages. These labeled data, though in different linguistic representations, share a substantial amount of semantic information with Chinese ones, and can be utilized to help classify Chinese Web pages. In this paper, we propose an information bottleneck based approach to address this cross-language classification problem. Our algorithm first translates all the Chinese Web pages to English. Then, all the Web pages, including Chinese and English ones, are encoded through an information bottleneck which can allow only limited information to pass. Therefore, in order to retain as much useful information as possible, the common part between Chinese and English Web pages is inclined to be encoded to the same code (i.e. class label), which makes the cross-language classification accurate. We evaluated our approach using the Web pages collected from Open Directory Project (ODP). The experimental results show that our method significantly improves several existing supervised and semi-supervised classifiers.