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.
As discussed, the efforts needed for gaining trust are extensive. A consequence might be that sellers concentrate on establishing reputation on one specific forum instead of several forums. It is therefore not expected that sellers are present on multiple forums. By assuming this, we support Motoyama' @cite_0 expectation of non-existing multiple accounts. In contrast, we disagree with @cite_27 who argue without providing any reasons that sellers would advertise on multiple marketplaces. We hypothesise ( 6 ), therefore, that the vast majority of actors are not operating on more than one forum.
@cite_0 @cite_27
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We study selling one indivisible object to multiple potential buyers. Depending on the objective of the seller, different selling mechanisms are desirable. The Vickrey auction with a truthful reserve price is optimal when the objective is efficiency (i.e., allocating the object to the party who values it the most). The Myerson auction is optimal when the objective is the seller's expected utility. These two objectives are generally in conflict, and cannot be maximized with one mechanism. In many real-world settings---such as privatization and competing electronic marketplaces---it is not clear that the objective should be either efficiency or seller's expected utility. Typically, one of these objectives should weigh more than the other, but both are important. We account for importance of both objectives by designing a new deterministic auction mechanism that maximizes expected social welfare subject to a minimum constraint on the seller's expected utility. This way the seller can expect to do well enough for himself, while maintaining the attractive properties of the mechanism. 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. 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 . When facilitating asynchronous discussion forums, should online instructors be encouraged to take a prominent 'sage on the stage' role, a more constructivist 'guide on the side' role, or an ultra low profile as 'the ghost in the wings'? There is no shortage of anecdotal advice on how to conduct discussion forums in online education, but there appears to be very little research available so far to back that advice up. In this study of an online astronomy program with approximately 200 participants, we investigated the way that the rate at which instructors post and how often those instructors initiate discussions correlate with several variables--student posting rates, lengths of discussion threads, and student survey responses concerning their educational experience. We found that the ways in which instructors post to forums can influence students' forum discussions and perceptions, but not always in expected ways. On average, frequent posting by instructors did not lead to more student postings, and the more the instructors posted, the shorter were the lengths of the discussions overall. On the other hand, while most students rated their educational experience highly, instructors who posted frequently were judged on average to be more enthusiastic and expert than those who did not. Clearly the number of student postings and the rate at which instructors participate are not simple indicators of the quality of forum discussions. We need to find more subtle measures of the effectiveness of asynchronous discussion forums for learning and teaching. Research shows that comment spamming (comments which are unsolicited, unrelated, abusive, hateful, commercial advertisements etc) in online discussion forums has become a common phenomenon in Web 2.0 applications and there is a strong need to counter or combat comment spamming. We present a method to automatically detect comment spammer in YouTube (largest and a popular video sharing website) forums. The proposed technique is based on mining comment activity log of a user and extracting patterns (such as time interval between subsequent comments, presence of exactly same comment across multiple unrelated videos) indicating spam behavior. We perform empirical analysis on data crawled from YouTube and demonstrate that the proposed method is effective for the task of comment spammer detection. The threat of hackers and data thieves has increased, though few have considered the ways they dispose of the information obtained through computer attacks. This exploratory study examines the nature of the market for stolen data using a qualitative analysis of 300 threads from six web forums run by and for data thieves. The results suggest that all manner of personal and financial data can be obtained through these markets at a fraction of their true value. In addition, there are distinct relationships between buyers and sellers that shape the relationships and structure of these markets. Policy implications for law enforcement intervention are also discussed. We consider the problem of a single seller repeatedly selling a single item to a single buyer (specifically, the buyer has a value drawn fresh from known distribution D in every round). Prior work assumes that the buyer is fully rational and will perfectly reason about how their bids today affect the seller's decisions tomorrow. In this work we initiate a different direction: the buyer simply runs a no-regret learning algorithm over possible bids. We provide a fairly complete characterization of optimal auctions for the seller in this domain. Specifically: - If the buyer bids according to EXP3 (or any "mean-based" learning algorithm), then the seller can extract expected revenue arbitrarily close to the expected welfare. This auction is independent of the buyer's valuation D , but somewhat unnatural as it is sometimes in the buyer's interest to overbid. - There exists a learning algorithm A such that if the buyer bids according to A then the optimal strategy for the seller is simply to post the Myerson reserve for D every round. - If the buyer bids according to EXP3 (or any "mean-based" learning algorithm), but the seller is restricted to "natural" auction formats where overbidding is dominated (e.g. Generalized First-Price or Generalized Second-Price), then the optimal strategy for the seller is a pay-your-bid format with decreasing reserves over time. Moreover, the seller's optimal achievable revenue is characterized by a linear program, and can be unboundedly better than the best truthful auction yet simultaneously unboundedly worse than the expected welfare. 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. 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. Abstract We study the performance of the English auction under different assumptions about the seller's degree of “Bayesian sophistication.” We define the effectiveness of an auction as the ratio between the expected revenue it generates for the seller and the expected valuation of the object to the bidder with the highest valuation (total surplus). We identify tight lower bounds on the effectiveness of the English auction for general private-values environments, and for private-values environments where bidders' valuations are non-negatively correlated. For example, when the seller faces 12 bidders who the seller believes have non-negatively correlated valuations whose expectations are at least as high as 60 of the maximal possible valuation, an English auction with no reserve price generates an expected price that is more than 80 of the value of the object to the bidder with the highest valuation. We study the computational challenge faced by a intermediary who attempts to profit from trade with a small number of buyers and sellers of some item. In the version of the problem that we study, the number of buyers and sellers is constant, but their joint distribution of the item’s value may be complicated. We consider discretized distributions, where the complexity parameter is the support size, or number of different prices that may occur. We show that maximizing the expected revenue is computationally tractable (via an LP) if we are allowed to use randomized mechanisms. For the deterministic case, we show how an optimal mechanism can be efficiently computed for the one-seller one-buyer case, but give a contrasting NP-completeness result for the one-seller two-buyer case. Stylometry is a method for identifying anonymous authors of anonymous texts by analyzing their writing style. While stylometric methods have produced impressive results in previous experiments, we wanted to explore their performance on a challenging dataset of particular interest to the security research community. Analysis of underground forums can provide key information about who controls a given bot network or sells a service, and the size and scope of the cybercrime underworld. Previous analyses have been accomplished primarily through analysis of limited structured metadata and painstaking manual analysis. However, the key challenge is to automate this process, since this labor intensive manual approach clearly does not scale. We consider two scenarios. The first involves text written by an unknown cybercriminal and a set of potential suspects. This is standard, supervised stylometry problem made more difficult by multilingual forums that mix l33t-speak conversations with data dumps. In the second scenario, you want to feed a forum into an analysis engine and have it output possible doppelgangers, or users with multiple accounts. While other researchers have explored this problem, we propose a method that produces good results on actual separate accounts, as opposed to data sets created by artificially splitting authors into multiple identities. For scenario 1, we achieve 77 to 84 accuracy on private messages. For scenario 2, we achieve 94 recall with 90 precision on blogs and 85.18 precision with 82.14 recall for underground forum users. We demonstrate the utility of our approach with a case study that includes applying our technique to the Carders forum and manual analysis to validate the results, enabling the discovery of previously undetected doppelganger accounts. One of the earliest and best known Internet reputation systems is run by eBay, which gathers comments from buyers and sellers about each other after each transaction. Examination of a large data set from 1999 reveals several interesting features. First, despite incentives to free ride, feedback was provided more than half the time. Second, well beyond reasonable expectation, it was almost always positive. Third, reputation profiles were predictive of future performance, though eBay's net feedback statistic is far from the best predictor available. Fourth, there was a high correlation between buyer and seller feedback, suggesting that the players reciprocate and ret aliate. Online discussion forums are a valuable means for users to resolve specific information needs, both interactively for the participants and statically for users who search browse over historical thread data. However, the complex structure of forum threads can make it difficult for users to extract relevant information. Automatically identifying whether the problem in a thread has been solved or not can help direct users to threads where the original problem has been solved, hence enhancing their prospects of solving their particular problem. In this paper, we investigate the task of Solvedness classification by exploiting the discourse structure of forum threads. Experimental results show that simple features derived from thread discourse structure can greatly boost the accuracy of Solvedness classification, which has been shown to be very difficult in previous research. Online religious forums allow individuals to meet and interact with others who share their faith, beliefs, and values from the privacy of their homes. Active membership in traditional religious organizations has been shown to fulfill important social needs and to be associated with a number of benefits for the individuals involved. The survey study we report here found that many of the self and social benefits derived from participation in local religious institutions also accrue for those who take part in virtual religious forums. These interactive online forums were found to attract both those who are actively engaged in their local religious organizations and those who are unaffiliated. We analyse the usage of quotes in forum.rpg.net, the largest online forum on tabletop roleplaying games. Quote usage appears pervasive and surprisingly consistent over time and users; it seems to have a role in aiding intra-thread navigation; and it reveals an underlying "social" structure in a community that otherwise lacks all trappings (from friends and followers to reputations) of today's social networks. This is the first work to investigate community structure and interaction through the lens of quotes in an online forum. The Message Passing Interface Forum (MPIF), with participation from over 40 organizations, has been meeting since November 1992 to discuss and define a set of library standards for message passing. MPIF is not sanctioned or supported by any official standards organization. The goal of the Message Passing Interface, simply stated, is to develop a widely used standard for writing message-passing programs. As such the interface should establish a practical, portable, efficient and flexible standard for message passing. , This is the final report, Version 1.0, of the Message Passing Interface Forum. This document contains all the technical features proposed for the interface. This copy of the draft was processed by LATEX on April 21, 1994. , Please send comments on MPI to mpi-comments@cs.utk.edu. Your comment will be forwarded to MPIF committee members who will attempt to respond. We study in this paper the Web forum crawling problem, which is a very fundamental step in many Web applications, such as search engine and Web data mining. As a typical user-created content (UCC), Web forum has become an important resource on the Web due to its rich information contributed by millions of Internet users every day. However, Web forum crawling is not a trivial problem due to the in-depth link structures, the large amount of duplicate pages, as well as many invalid pages caused by login failure issues. In this paper, we propose and build a prototype of an intelligent forum crawler, iRobot, which has intelligence to understand the content and the structure of a forum site, and then decide how to choose traversal paths among different kinds of pages. To do this, we first randomly sample (download) a few pages from the target forum site, and introduce the page content layout as the characteristics to group those pre-sampled pages and re-construct the forum's sitemap. After that, we select an optimal crawling path which only traverses informative pages and skips invalid and duplicate ones. The extensive experimental results on several forums show the performance of our system in the following aspects: 1) Effectiveness - Compared to a generic crawler, iRobot significantly decreases the duplicate and invalid pages; 2) Efficiency - With a small cost of pre-sampling a few pages for learning the necessary knowledge, iRobot saves substantial network bandwidth and storage as it only fetches informative pages from a forum site; and 3) Long threads that are divided into multiple pages can be re-concatenated and archived as a whole thread, which is of great help for further indexing and data mining. MPI (Message Passing Interface) is a specification for a standard library for message passing that was defined by the MPI Forum, a broadly based group of parallel computer vendors, library writers, and applications specialists. Multiple implementations of MPI have been developed. In this paper, we describe MPICH, unique among existing implementations in its design goal of combining portability with high performance. We document its portability and performance and describe the architecture by which these features are simultaneously achieved. We also discuss the set of tools that accompany the free distribution of MPICH, which constitute the beginnings of a portable parallel programming environment. A project of this scope inevitably imparts lessons about parallel computing, the specification being followed, the current hardware and software environment for parallel computing, and project management; we describe those we have learned. Finally, we discuss future developments for MPICH, including those necessary to accommodate extensions to the MPI Standard now being contemplated by the MPI Forum. Online discussion forums are a valuable resource for people looking to find information, discuss ideas, and get advice on the Internet. Unfortunately, many forums have too much activity and information available, resulting in information overload. Moderation systems are implemented in some forums as a way to handle this problem, but due to sparsity issues, they are often not sufficient. We describe a novel method for learning from past moderations to develop a classifier that can perform automated moderation and thus address the sparsity problem. Additionally, we discuss the possibility of training a moderating classifier on a moderated forum and then applying it to an otherwise unmoderated forum.