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.
Sood and Enbody @cite_15 provide a more detailed estimation of rates charged per credit card number. Numbers from the USA cost @math 10 on average, from Canada @math 7 and from the UK @math 8. Classified according to credit card types, a classic or standard credit card number from the USA or Canada costs @math 10, a gold card @math 20 and an Amex @math 10. Classic and Amex cards are the cheapest in the listing of Sood and Enbody @cite_15 . Nevertheless, they are still more expensive than the lower limits of their quoted price range ( @math 5). However, it is not ideal that these rates have not been observed but estimated and it remains unclear on which basis they have been calculated.
@cite_15
2009229808
In the generalized Russian cards problem, we have a card deck @math of @math cards and three participants, Alice, Bob, and Cathy, dealt @math , @math , and @math cards, respectively. Once the cards are dealt, Alice and Bob wish to privately communicate their hands to each other via public announcements, without the advantage of a shared secret or public key infrastructure. Cathy should remain ignorant of all but her own cards after Alice and Bob have made their announcements. Notions for Cathy's ignorance in the literature range from Cathy not learning the fate of any individual card with certainty (weak @math -security) to not gaining any probabilistic advantage in guessing the fate of some set of @math cards (perfect @math -security). As we demonstrate, the generalized Russian cards problem has close ties to the field of combinatorial designs, on which we rely heavily, particularly for perfect security notions. Our main result establishes an equivalence between perfectly @math -secure strategies and @math -designs on @math points with block size @math , when announcements are chosen uniformly at random from the set of possible announcements. We also provide construction methods and example solutions, including a construction that yields perfect @math -security against Cathy when @math . We leverage a known combinatorial design to construct a strategy with @math , @math , and @math that is perfectly @math -secure. Finally, we consider a variant of the problem that yields solutions that are easy to construct and optimal with respect to both the number of announcements and level of security achieved. Moreover, this is the first method obtaining weak @math -security that allows Alice to hold an arbitrary number of cards and Cathy to hold a set of @math cards. Alternatively, the construction yields solutions for arbitrary @math , @math and any @math . RFID-enabled credit cards are widely deployed in the United States and other countries, but no public study has thoroughly analyzed the mechanisms that provide both security and privacy. Using samples from a variety of RFID-enabled credit cards, our study observes that (1) the cardholder's name and often credit card number and expiration are leaked in plaintext to unauthenticated readers, (2) our homemade device costing around $150 effectively clones one type of skimmed cards thus providing a proof-of-concept implementation for the RF replay attack, (3) information revealed by the RFID transmission cross contaminates the security of RFID and non-RFID payment contexts, and (4) RFID-enabled credit cards are susceptible in various degrees to a range of other traditional RFID attacks such as skimming and relaying. In the generalized Russian cards problem, the three players Alice, Bob and Cath draw @math a , b and @math c cards, respectively, from a deck of @math a + b + c cards. Players only know their own cards and what the deck of cards is. Alice and Bob are then required to communicate their hand of cards to each other by way of public messages. For a natural number @math k , the communication is said to be @math k -safe if Cath does not learn whether or not Alice holds any given set of at most @math k cards that are not Cath's, a notion originally introduced as weak @math k -security by Swanson and Stinson. An elegant solution by Atkinson views the cards as points in a finite projective plane. We propose a general solution in the spirit of Atkinson's, although based on finite vector spaces rather than projective planes, and call it the geometric protocol'. Given arbitrary @math c , k > 0 , this protocol gives an informative and @math k -safe solution to the generalized Russian cards problem for infinitely many values of @math ( a , b , c ) with @math b = O ( a c ) . This improves on the collection of parameters for which solutions are known. In particular, it is the first solution which guarantees @math k -safety when Cath has more than one card. Credit card is one of the popular modes of payment for electronic transactions in many developed and developing countries. Invention of credit cards has made online transactions seamless, easier, comfortable and convenient. However, it has also provided new fraud opportunities for criminals, and in turn, increased fraud rate. The global impact of credit card fraud is alarming, millions of US dollars have been lost by many companies and individuals. Furthermore, cybercriminals are innovating sophisticated techniques on a regular basis, hence, there is an urgent task to develop improved and dynamic techniques capable of adapting to rapidly evolving fraudulent patterns. Achieving this task is very challenging, primarily due to the dynamic nature of fraud and also due to lack of dataset for researchers. This paper presents a review of improved credit card fraud detection techniques. Precisely, this paper focused on recent Machine Learning based and Nature Inspired based credit card fraud detection techniques proposed in literature. This paper provides a picture of recent trend in credit card fraud detection. Moreover, this review outlines some limitations and contributions of existing credit card fraud detection techniques, it also provides necessary background information for researchers in this domain. Additionally, this review serves as a guide and stepping stone for financial institutions and individuals seeking for new and effective credit card fraud detection techniques. We present the first formal mathematical presentation of the generalized Russian cards problem, and provide rigorous security definitions that capture both basic and extended versions of weak and perfect security notions. In the generalized Russian cards problem, three players, Alice, Bob, and Cathy, are dealt a deck of @math cards, each given @math , @math , and @math cards, respectively. The goal is for Alice and Bob to learn each other's hands via public communication, without Cathy learning the fate of any particular card. The basic idea is that Alice announces a set of possible hands she might hold, and Bob, using knowledge of his own hand, should be able to learn Alice's cards from this announcement, but Cathy should not. Using a combinatorial approach, we are able to give a nice characterization of informative strategies (i.e., strategies allowing Bob to learn Alice's hand), having optimal communication complexity, namely the set of possible hands Alice announces must be equivalent to a large set of @math -designs, where @math . We also provide some interesting necessary conditions for certain types of deals to be simultaneously informative and secure. That is, for deals satisfying @math for some @math , where @math and the strategy is assumed to satisfy a strong version of security (namely perfect @math -security), we show that @math and hence @math . We also give a precise characterization of informative and perfectly @math -secure deals of the form @math satisfying @math involving @math -designs. This paper studies a cooperative card game called Hanabi from an algorithmic combinatorial game theory viewpoint. The aim of the game is to play cards from @math to @math in increasing order (this has to be done independently in @math different colors). Cards are drawn from a deck one by one. Drawn cards are either immediately played, discarded or stored for future use (overall each player can store up to @math cards). We introduce a simplified mathematical model of a single-player version of the game, and show several complexity results: the game is intractable in a general setting, but becomes tractable (and even linear) in some interesting restricted cases (i.e., for small values of @math and @math ). Credit card fraud has seen rampant increase in the past years, as customers use credit cards and similar financial instruments frequently. Both online and brick-and-mortar outfits repeatedly fall victim to cybercriminals who siphon off credit card information in bulk. Despite the many and creative ways that attackers use to steal and trade credit card information, the stolen information can rarely be used to withdraw money directly, due to protection mechanisms such as PINs and cash advance limits. As such, cybercriminals have had to devise more advanced monetization schemes to work around the current restrictions. One monetization scheme that has been steadily gaining traction are reshipping scams. In such scams, cybercriminals purchase high-value or highly-demanded products from online merchants using stolen payment instruments, and then ship the items to a credulous citizen. This person, who has been recruited by the scammer under the guise of "work-from-home" opportunities, then forwards the received products to the cybercriminals, most of whom are located overseas. Once the goods reach the cybercriminals, they are then resold on the black market for an illicit profit. Due to the intricacies of this kind of scam, it is exceedingly difficult to trace, stop, and return shipments, which is why reshipping scams have become a common means for miscreants to turn stolen credit cards into cash. In this paper, we report on the first large-scale analysis of reshipping scams, based on information that we obtained from multiple reshipping scam websites. We provide insights into the underground economy behind reshipping scams, such as the relationships among the various actors involved, the market size of this kind of scam, and the associated operational churn. We find that there exist prolific reshipping scam operations, with one having shipped nearly 6,000 packages in just 9 months of operation, exceeding 7.3 million US dollars in yearly revenue, contributing to an overall reshipping scam revenue of an estimated 1.8 billion US dollars per year. Finally, we propose possible approaches to intervene and disrupt reshipping scam services. Botnets, networks of malware-infected machines (bots) that are controlled by an adversary, are the root cause of a large number of security problems on the Internet. A particularly sophisticated and insidious type of bot is Torpig, a malware program designed to harvest sensitive information (such as bank account and credit-card data) from its victims. In this article, the authors report on their efforts to take control of the Torpig botnet and study its operations for a period of 10 days. During this time, they observed more than 180,000 infections and recorded almost 70 Gbytes of data that the bots collected. They also report on what happened in the year that has passed since they lost control of the Torpig botnet. Consider the following method of card shuffling. Start with a deck of N cards numbered 1 through N. Fix a parameter p between 0 and 1. In this model a "shuffle" consists of uniformly selecting a pair of adjacent cards and then flipping a coin that is heads with probability p. If the coin comes up heads, then we arrange the two cards so that the lower-numbered card comes before the higher-numbered card. If the coin comes up tails, then we arrange the cards with the higher-numbered card first. In this paper we prove that for all p ≠ 1 2, the mixing time of this card shuffling is O(N^2), as conjectured by Diaconis and Ram (2000). Our result is a rare case of an exact estimate for the convergence rate of the Metropolis algorithm. A novel feature of our proof is that the analysis of an infinite (asymmetric exclusion) process plays an essential role in bounding the mixing time of a finite process. This paper deals with finding outliers (exceptions) in large, multidimensional datasets. The identification of outliers can lead to the discovery of truly unexpected knowledge in areas such as electronic commerce, credit card fraud, and even the analysis of performance statistics of professional athletes. Existing methods that we have seen for finding outliers can only deal efficiently with two dimensions attributes of a dataset. In this paper, we study the notion of DB (distance-based) outliers. Specifically, we show that (i) outlier detection can be done efficiently for large datasets, and for k-dimensional datasets with large values of k (e.g., @math ); and (ii), outlier detection is a meaningful and important knowledge discovery task.First, we present two simple algorithms, both having a complexity of @math , k being the dimensionality and N being the number of objects in the dataset. These algorithms readily support datasets with many more than two attributes. Second, we present an optimized cell-based algorithm that has a complexity that is linear with respect to N, but exponential with respect to k. We provide experimental results indicating that this algorithm significantly outperforms the two simple algorithms for @math . Third, for datasets that are mainly disk-resident, we present another version of the cell-based algorithm that guarantees at most three passes over a dataset. Again, experimental results show that this algorithm is by far the best for @math . Finally, we discuss our work on three real-life applications, including one on spatio-temporal data (e.g., a video surveillance application), in order to confirm the relevance and broad applicability of DB outliers. We consider settings in which we wish to incentivize myopic agents (such as Airbnb landlords, who may emphasize short-term profits and property safety) to treat arriving clients fairly, in order to prevent overall discrimination against individuals or groups. We model such settings in both classical and contextual bandit models in which the myopic agents maximize rewards according to current empirical averages, but are also amenable to exogenous payments that may cause them to alter their choices. Our notion of fairness asks that more qualified individuals are never (probabilistically) preferred over less qualified ones []. We investigate whether it is possible to design inexpensive subsidy or payment schemes for a principal to motivate myopic agents to play fairly in all or almost all rounds. When the principal has full information about the state of the myopic agents, we show it is possible to induce fair play on every round with a subsidy scheme of total cost @math (for the classic setting with @math arms, @math , and for the @math -dimensional linear contextual setting @math ). If the principal has much more limited information (as might often be the case for an external regulator or watchdog), and only observes the number of rounds in which members from each of the @math groups were selected, but not the empirical estimates maintained by the myopic agent, the design of such a scheme becomes more complex. We show both positive and negative results in the classic and linear bandit settings by upper and lower bounding the cost of fair subsidy schemes. We investigate the minimization of Newton's functional for the problem of the body of minimal resistance with maximal height @math cite butt in the class of convex developable functions defined in a disc. This class is a natural candidate to find a (non-radial) minimizer in accordance with the results of cite lrp2. We prove that the minimizer in this class has a minimal set in the form of a regular polygon with @math sides centered in the disc, and numerical experiments indicate that the natural number @math is a non-decreasing function of @math . The corresponding functions all achieve a lower value of the functional than the optimal radially symmetric function with the same height @math . Phishing is a form of identity theft that occurs when a malicious Web site impersonates a legitimate one in order to acquire sensitive information such as passwords, account details, or credit card numbers.Though there are several anti-phishing software and techniques for detecting potential phishing attempts in emails and detecting phishing contents on websites, phishers come up with new and hybrid techniques to circumvent the available software and techniques. In cryo-electron microscopy, the three-dimensional (3D) electric potentials of an ensemble of molecules are projected along arbitrary viewing directions to yield noisy two-dimensional images. The volume maps representing these potentials typically exhibit a great deal of structural variability, which is described by their 3D covariance matrix. Typically, this covariance matrix is approximately low rank and can be used to cluster the volumes or estimate the intrinsic geometry of the conformation space. We formulate the estimation of this covariance matrix as a linear inverse problem, yielding a consistent least-squares estimator. For @math images of size @math -by- @math pixels, we propose an algorithm for calculating this covariance estimator with computational complexity @math , where the condition number @math is empirically in the range 10--200. Its efficiency relies on the observation that the normal equations are equivalent to a deconvolution problem in six dimensions.... In the framework of Wegman and Carter, a @math -independent hash function maps any @math keys independently. It is known that 5-independent hashing provides good expected performance in applications such as linear probing and second moment estimation for data streams. The classic @math -independent hash function evaluates a degree 4 polynomial over a prime field containing the key domain @math . Here we present an efficient 5-independent hash function that uses no multiplications. Instead, for any parameter @math , we make @math lookups in tables of size @math . In experiments on different computers, our scheme gained factors of 1.8 to 10 in speed over the polynomial method. We also conducted experiments on the performance of hash functions inside the above applications. In particular, we give realistic examples of inputs that make the most popular 2-independent hash function perform quite poorly. This illustrates the advantage of using schemes with provably good expected performance for all inputs. A Monte Carlo algorithm is presented that updates large clusters of spins simultaneously in systems at and near criticality. We demonstrate its efficiency in the two-dimensional @math @math models for @math (Ising) and @math ( @math ) at their critical temperatures, and for @math (Heisenberg) with correlation lengths around 10 and 20. On lattices up to @math no sign of critical slowing down is visible with autocorrelation times of 1-2 steps per spin for estimators of long-range quantities. We consider the problem of estimating the unknown response function in the multichannel deconvolution model with a boxcar-like kernel which is of particular interest in signal processing. It is known that, when the number of channels is finite, the precision of reconstruction of the response function increases as the number of channels @math grow (even when the total number of observations @math for all channels @math remains constant) and this requires that the parameter of the channels form a Badly Approximable @math -tuple. Recent advances in data collection and recording techniques made it of urgent interest to study the case when the number of channels @math grow with the total number of observations @math . However, in real-life situations, the number of channels @math usually refers to the number of physical devices and, consequently, may grow to infinity only at a slow rate as @math . When @math grows slowly as @math increases, we develop a procedure for the construction of a Badly Approximable @math -tuple on a specified interval, of a non-asymptotic length, together with a lower bound associated with this @math -tuple, which explicitly shows its dependence on @math as @math is growing. This result is further used for the evaluation of the @math -risk of the suggested adaptive wavelet thresholding estimator of the unknown response function and, furthermore, for the choice of the optimal number of channels @math which minimizes the @math -risk. We study the problem of distribution to real-value regression, where one aims to regress a mapping @math that takes in a distribution input covariate @math (for a non-parametric family of distributions @math ) and outputs a real-valued response @math . This setting was recently studied, and a "Kernel-Kernel" estimator was introduced and shown to have a polynomial rate of convergence. However, evaluating a new prediction with the Kernel-Kernel estimator scales as @math . This causes the difficult situation where a large amount of data may be necessary for a low estimation risk, but the computation cost of estimation becomes infeasible when the data-set is too large. To this end, we propose the Double-Basis estimator, which looks to alleviate this big data problem in two ways: first, the Double-Basis estimator is shown to have a computation complexity that is independent of the number of of instances @math when evaluating new predictions after training; secondly, the Double-Basis estimator is shown to have a fast rate of convergence for a general class of mappings @math . This paper is concerned with public communication with the Russian Cards protocol. First, a couple of small flaws in [10] are corrected. Then an improved Russian Cards protocol is presented. As a case study, R(6, 31)(6 players and 31 cards) protocol is used to generate a common password for 5 parties who wish to access a shared file over the Internet. Abstract We predict mortgage default by applying convolutional neural networks to consumer transaction data. For each consumer we have the balances of the checking account, savings account, and the credit card, in addition to the daily number of transactions on the checking account, and amount transferred into the checking account. With no other information about each consumer we are able to achieve a ROC AUC of 0.918 for the networks, and 0.926 for the networks in combination with a random forests classifier.