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
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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. 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. As product prices become increasingly available on the World Wide Web, consumers attempt to understand how corporations vary these prices over time. However, corporations change prices based on proprietary algorithms and hidden variables (e.g., the number of unsold seats on a flight). Is it possible to develop data mining techniques that will enable consumers to predict price changes under these conditions?This paper reports on a pilot study in the domain of airline ticket prices where we recorded over 12,000 price observations over a 41 day period. When trained on this data, Hamlet --- our multi-strategy data mining algorithm --- generated a predictive model that saved 341 simulated passengers @math 320,572 in our simulation, thus H AMLET 's savings were 61.8 of optimal. The algorithm's savings of $198,074 represents an average savings of 23.8 for the 341 passengers for whom savings are possible. Overall, HAMLET saved 4.4 of the ticket price averaged over the entire set of 4,488 simulated passengers. Our pilot study suggests that mining of price data available over the web has the potential to save consumers substantial sums of money per annum. 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. Magnetic stripe cards are in common use for electronic payments and cash withdrawal. Reported incidents document that criminals easily pickpocket cards or skim them by swiping them through additional card readers. Personal identification numbers (PINs) are obtained by shoulder surfing, through the use of mirrors or concealed miniature cameras. Both elements, the PIN and the card, are generally sufficient to give the criminal full access to the victim's account. In this paper, we present alternative PIN entry methods to which we refer as cognitive trapdoor games. These methods make it significantly harder for a criminal to obtain PINs even if he fully observes the entire input and output of a PIN entry procedure. We also introduce the idea of probabilistic cognitive trapdoor games, which offer resilience to shoulder surfing even if the criminal records a PIN entry procedure with a camera. We studied the security as well as the usability of our methods, the results of which we also present in the paper. 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 chapter documents what we believe to be the first systematic study of the costs of cybercrime. The initial workshop paper was prepared in response to a request from the UK Ministry of Defence following scepticism that previous studies had hyped the problem. For each of the main categories of cybercrime we set out what is and is not known of the direct costs, indirect costs and defence costs – both to the UK and to the world as a whole. We distinguish carefully between traditional crimes that are now “cyber” because they are conducted online (such as tax and welfare fraud); transitional crimes whose modus operandi has changed substantially as a result of the move online (such as credit card fraud); new crimes that owe their existence to the Internet; and what we might call platform crimes such as the provision of botnets which facilitate other crimes rather than being used to extract money from victims directly. As far as direct costs are concerned, we find that traditional offences such as tax and welfare fraud cost the typical citizen in the low hundreds of pounds euros dollars a year; transitional frauds cost a few pounds euros dollars; while the new computer crimes cost in the tens of pence cents. However, the indirect costs and defence costs are much higher for transitional and new crimes. For the former they may be roughly comparable to what the criminals earn, while for the latter they may be an order of magnitude more. As a striking example, the botnet behind a third of the spam sent in 2010 earned its owners around $2.7 million, while worldwide expenditures on spam prevention probably exceeded a billion dollars. We are extremely inefficient at fighting cybercrime; or to put it another way, cyber-crooks are like terrorists or met al thieves in that their activities impose disproportionate costs on society. Some of the reasons for this are well-known: cybercrimes are global and have strong externalities, while traditional crimes such as burglary and car theft are local, and the associated equilibria have emerged after many years of optimisation. As for the more direct question of what should be done, our figures suggest that we should spend less in anticipation of cybercrime (on antivirus, firewalls, etc.) and more in response – that is, on the prosaic business of hunting down cyber-criminals and throwing them in jail. The paper describes a methodology that has been implemented in a major British airline to find the optimal price to charge for airline tickets under one-way pricing. An analytical model has been developed to describe the buying behaviour of customers for flights over the selling period. Using this model and a standard analytical method for constrained optimization, we can find an expression for the optimal price structure for a flight. The expected number of bookings made on each day of the selling period and in each fare class given these prices can then be easily calculated. A simulation model is used to find the confidence ranges on the numbers of bookings and these ranges can be used to regulate the sale of tickets. A procedure to update the price structure based on the remaining capacity has also been developed. 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. Let f be a random Boolean formula that is an instance of 3-SAT. We consider the problem of computing the least real number k such that if the ratio of the number of clauses over the number of variables of f strictly exceeds k , then f is almost certainly unsatisfiable. By a well-known and more or less straightforward argument, it can be shown that kF5.191. This upper bound was improved by to 4.758 by first providing new improved bounds for the occupancy problem. There is strong experimental evidence that the value of k is around 4.2. In this work, we define, in terms of the random formula f, a decreasing sequence of random variables such that, if the expected value of any one of them converges to zero, then f is almost certainly unsatisfiable. By letting the expected value of the first term of the sequence converge to zero, we obtain, by simple and elementary computations, an upper bound for k equal to 4.667. From the expected value of the second term of the sequence, we get the value 4.601q . In general, by letting the U This work was performed while the first author was visiting the School of Computer Science, Carleton Ž University, and was partially supported by NSERC Natural Sciences and Engineering Research Council . of Canada , and by a grant from the University of Patras for sabbatical leaves. The second and third Ž authors were supported in part by grants from NSERC Natural Sciences and Engineering Research . Council of Canada . During the last stages of this research, the first and last authors were also partially Ž . supported by EU ESPRIT Long-Term Research Project ALCOM-IT Project No. 20244 . †An extended abstract of this paper was published in the Proceedings of the Fourth Annual European Ž Symposium on Algorithms, ESA’96, September 25]27, 1996, Barcelona, Spain Springer-Verlag, LNCS, . pp. 27]38 . That extended abstract was coauthored by the first three authors of the present paper. Correspondence to: L. M. Kirousis Q 1998 John Wiley & Sons, Inc. CCC 1042-9832r98r030253-17 253 We introduce the following version of bin packing. The items are of two types (black and white), and in each bin the item types must alternate. We mostly investigate the online scenario. We study the competitiveness of some classical algorithms (First Best Worst Next Fit, Harmonic) — they do not perform very well — and for all online algorithms we also prove the universal lower bound (1+ 1 2 .7213 ) which significantly exceeds the known upper bound 1.58889 on classical online bin packing. We also design an online algorithm which is 3-competitive in the absolute sense. A 2.5-approximation algorithm and an APTAS is also given for the offline version. In this paper, we set out to compare several techniques that can be used in the analysis of imbalanced credit scoring data sets. In a credit scoring context, imbalanced data sets frequently occur as the number of defaulting loans in a portfolio is usually much lower than the number of observations that do not default. As well as using traditional classification techniques such as logistic regression, neural networks and decision trees, this paper will also explore the suitability of gradient boosting, least square support vector machines and random forests for loan default prediction.Five real-world credit scoring data sets are used to build classifiers and test their performance. In our experiments, we progressively increase class imbalance in each of these data sets by randomly under-sampling the minority class of defaulters, so as to identify to what extent the predictive power of the respective techniques is adversely affected. The performance criterion chosen to measure this effect is the area under the receiver operating characteristic curve (AUC); Friedman's statistic and Nemenyi post hoc tests are used to test for significance of AUC differences between techniques.The results from this empirical study indicate that the random forest and gradient boosting classifiers perform very well in a credit scoring context and are able to cope comparatively well with pronounced class imbalances in these data sets. We also found that, when faced with a large class imbalance, the C4.5 decision tree algorithm, quadratic discriminant analysis and k-nearest neighbours perform significantly worse than the best performing classifiers. Abstract In this paper, we study the 1-dimensional on-line bin packing problem. A list of pieces, each of size between zero and unity are to be packed, in order of their arrival, into a minimum number of unit-capacity bins. We present a new linear-time algorithm, the Modified Harmonic Algorithm and show, by a novel use of weighting functions, that it has an asymptotic worst-case performance ratio less than 3 2 + 1 9 + 1 222 = 1.(615) ∗ . We show that for a large class of linear-time on-line algorithms including the Modified Harmonic Algorithm, the performance ratio is at least 3 2 + 1 9 = 1.61 ∗ . Then we show how to successively construct classes of improved linear-time on-line algorithms. For any algorithm in any of these classes, the performance ratio is at least 3 2 + 1 12 = 1.583 ∗ . We present an improved algorithm called Modified Harmonic-2 with performance ratio 1.612 … and present an approach to construct linear-time on-line algorithms with better performance ratios. The analysis of Modified Harmonic-2 is omitted because it is very similar to that of Modified Harmonic, but it is substantially more complicated. Our results extend to orthogonal packings in two dimensions. Suppose that there are n jobs and n machines and it costs cij to execute job i on machine j. The assignment problem concerns the determination of a one-to-one assignment of jobs onto machines so as to minimize the cost of executing all the jobs. When the cij are independent and identically distributed exponentials of mean 1, Parisi [Technical Report cond-mat 9801176, xxx LANL Archive, 1998] made the beautiful conjecture that the expected cost of the minimum assignment equals @math . Coppersmith and Sorkin [Random Structures Algorithms 15 (1999), 113–144] generalized Parisi's conjecture to the average value of the smallest k-assignment when there are n jobs and m machines. Building on the previous work of Sharma and Prabhakar [Proc 40th Annu Allerton Conf Communication Control and Computing, [2002], 657–666] and Nair [Proc 40th Annu Allerton Conf Communication Control and Computing, [2002], 667–673], we resolve the Parisi and Coppersmith-Sorkin conjectures. In the process we obtain a number of combinatorial results which may be of general interest.© 2005 Wiley Periodicals, Inc. Random Struct. Alg. 2005Supported by the Stanford Graduate Fellowship and Stanford Networking Research Center Grant 1005544-1-WAAXI.Supported in part by the NSF Grant ANI-9985446.Supported by Stanford Office of Technology Grant 2DTA112, Stanford Networking Research Center Grant 1005545-1-WABCJ, and NSF Grant ANI-9985446. We revisit the classic online bin packing problem studied in the half-century. In this problem, items of positive sizes no larger than 1 are presented one by one to be packed into subsets called bins of total sizes no larger than 1, such that every item is assigned to a bin before the next item is presented. We use online partitioning of items into classes based on sizes, as in previous work, but we also apply a new method where items of one class can be packed into more than two types of bins, where a bin type is defined according to the number of such items grouped together. Additionally, we allow the smallest class of items to be packed in multiple kinds of bins, and not only into their own bins. We combine this with the approach of packing of sufficiently big items according to their exact sizes. Finally, we simplify the analysis of such algorithms, allowing the analysis to be based on the most standard weight functions. This simplified analysis allows us to study the algorithm which we defined based on all these ideas. This leads us to the design and analysis of the first algorithm of asymptotic competitive ratio strictly below 1.58, specifically, we break this barrier by providing an algorithm AH (Advanced Harmonic) whose asymptotic competitive ratio does not exceed 1.57829. Our main contribution is the introduction of the simple analysis based on weight function to analyze the state of the art online algorithms for the classic online bin packing problem. The previously used analytic tool named weight system was too complicated for the community in this area to adjust it for other problems and other algorithmic tools that are needed in order to improve the current best algorithms. We show that the weight system based analysis is not needed for the analysis of the current algorithms for the classic online bin packing problem. The importance of a simple analysis is demonstrated by analyzing several new features together with all existing techniques, and by proving a better competitive ratio than the previously best one. We generalize an empirical likelihood approach to deal with missing data to a model of consumer credit scoring. An application to recent consumer credit data shows that our procedure yields parameter estimates which are significantly different (both statistically and economically) from the case where customers who were refused credit are ignored. This has obvious implications for commercial banks as it shows that refused customers should not be ignored when developing scorecards for the retail business. We also show that forecasts of defaults derived from the method proposed in this paper improve upon the standard ones when refused customers do not enter the estimation data set. In this paper we consider the Online Bin Packing Problem in three variants: Circles in Squares, Circles in Isosceles Right Triangles, and Spheres in Cubes. The two first ones receive an online sequence of circles (items) of different radii while the third one receive an online sequence of spheres (items) of different radii, and they want to pack the items into the minimum number of unit squares, isosceles right triangles of leg length one, and unit cubes, respectively. For Online Circle Packing in Squares, we improve the previous best-known competitive ratio for the bounded space version, when at most a constant number of bins can be open at any given time, from 2.439 to 2.3536. For Online Circle Packing in Isosceles Right Triangles and Online Sphere Packing in Cubes we show bounded space algorithms of asymptotic competitive ratios 2.5490 and 3.5316, respectively, as well as lower bounds of 2.1193 and 2.7707 on the competitive ratio of any online bounded space algorithm for these two problems. We also considered the online unbounded space variant of these three problems which admits a small reorganization of the items inside the bin after their packing, and we present algorithms of competitive ratios 2.3105, 2.5094, and 3.5146 for Circles in Squares, Circles in Isosceles Right Triangles, and Spheres in Cubes, respectively. Modern smartcards, capable of sophisticated cryptography, provide a high assurance of tamper resistance and are thus commonly used in payment applications. Although extracting secrets out of smartcards requires resources beyond the means of many would-be thieves, the manner in which they are used can be exploited for fraud. Cardholders authorize financial transactions by presenting the card and disclosing a PIN to a terminal without any assurance as to the amount being charged or who is to be paid, and have no means of discerning whether the terminal is authentic or not. Even the most advanced smartcards cannot protect customers from being defrauded by the simple relaying of data from one location to another. We describe the development of such an attack, and show results from live experiments on the UK's EMV implementation, Chip & PIN. We discuss previously proposed defences, and show that these cannot provide the required security assurances. A new defence based on a distance bounding protocol is described and implemented, which requires only modest alterations to current hardware and software. As far as we are aware, this is the first complete design and implementation of a secure distance bounding protocol. Future smartcard generations could use this design to provide cost-effective resistance to relay attacks, which are a genuine threat to deployed applications. We also discuss the security-economics impact to customers of enhanced authentication mechanisms. Given the prevalence of social media websites, one challenge facing computer vision researchers is to devise methods to search for persons of interest among the billions of shared photos on these websites. Despite significant progress in face recognition, searching a large collection of unconstrained face images remains a difficult problem. To address this challenge, we propose a face search system which combines a fast search procedure, coupled with a state-of-the-art commercial off the shelf (COTS) matcher, in a cascaded framework. Given a probe face, we first filter the large gallery of photos to find the top- @math most similar faces using features learned by a convolutional neural network. The @math retrieved candidates are re-ranked by combining similarities based on deep features and those output by the COTS matcher. We evaluate the proposed face search system on a gallery containing @math million web-downloaded face images. Experimental results demonstrate that while the deep features perform worse than the COTS matcher on a mugshot dataset (93.7 percent versus 98.6 percent TAR@FAR of 0.01 percent), fusing the deep features with the COTS matcher improves the overall performance ( @math percent TAR@FAR of 0.01 percent). This shows that the learned deep features provide complementary information over representations used in state-of-the-art face matchers. On the unconstrained face image benchmarks, the performance of the learned deep features is competitive with reported accuracies. LFW database: @math percent accuracy under the standard protocol and @math percent TAR@FAR of @math percent under the BLUFR protocol; IJB-A benchmark: @math percent TAR@FAR of @math percent (verification), rank 1 retrieval of @math percent (closed-set search), @math percent FNIR@FAR of @math percent (open-set search). The proposed face search system offers an excellent trade-off between accuracy and scalability on galleries with millions of images. Additionally, in a face search experiment involving photos of the Tsarnaev brothers, convicted of the Boston Marathon bombing, the proposed cascade face search system could find the younger brother's (Dzhokhar Tsarnaev) photo at rank @math in @math second on a @math M gallery and at rank @math in @math seconds on an @math M gallery. If asked whether certain digits in numbers collected randomly from, for example, the front pages of newspapers or from stock-market prices should occur more often than others, most people would think not. Nonetheless, in 1881, the astronomer and mathematician Simon Newcomb published a two-page article in the American Journal of Mathematics reporting an example of exactly that pheonomenon. Newcomb described his observation that books of logarithms in the library were quite dirty at the beginning and progressively cleaner throughout. From this he inferred that fellow scientists using the logarithm tables were looking up numbers starting with 1 more often than numbers starting with 2, numbers with first digit 2 more often than 3, and so on. After a short heuristic argument, Newcomb concluded that the probability that a number has a particular first significant digit (that is, first nonzero digit) d can be calculated as follows: Prob (first significant digit = d) = log10 (1 + 1 d), d = 1,2,...,9