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.
Credit card information is generally divided into three groups: , , and @cite_29 . Credit card numbers (also known as ) include at least the information printed on the card, that is, actual credit card number, cardholder name, expiration date and security code on the back of the card (not to be confused with CVV), and sometimes, the billing address and phone number. Dumps denote information from the tracks on the magnetic stripe of a card. These data are required to clone physical credit cards. Fullz provide further information on the cardholder including, for example, date of birth or social security number @cite_29 .
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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. This paper proposes a novel approach that applies neural network forecasting to security for closed-loop prepaid cards based on low-cost technologies such as RFID and 1-Wire. The security vulnerability of low-cost RFID closed-loop prepaid card systems originates mostly from the card itself. Criminal organizations counterfeit or clone card data. Although high-security prepaid cards exist, they are often too expensive for transport ticketing, and even their security is not guaranteed for a well-defined period of time. Therefore, data encryption systems are used widely against counterfeiting with success. However, it has not been possible to develop countermeasures with comparable success against cloning. Our proposed security application uses neural network forecasting to determine the recharge day behavioral characteristics of the cardholder and predict the next time the cardholder will recharge their card. Based on the prediction for the recharge time, the expiration date of the low-cost RFID prepaid card is defined, which is a good countermeasure against cloning. FTDNN, LRNN, and NARX network architectures with one hidden layer are considered in this research. The effects of the network architecture, the number of neurons, the training algorithm, and the prediction performance function on the recharge day forecast are investigated. Experimental results confirm the accuracy of the recharge time forecast and confirm countermeasures against cloning. Our proposed security approach with neural network forecasting is applied with success to the Turkish public transport without an online backend system. 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. In this paper, we report on a new approach for enhancing security and privacy in certain RFID applications whereby location or location-related information (such as speed) can serve as a legitimate access context. Examples of these applications include access cards, toll cards, credit cards, and other payment tokens. We show that location awareness can be used by both tags and back-end servers for defending against unauthorized reading and relay attacks on RFID systems. On the tag side, we design a location-aware selective unlocking mechanism using which tags can selectively respond to reader interrogations rather than doing so promiscuously. On the server side, we design a location-aware secure transaction verification scheme that allows a bank server to decide whether to approve or deny a payment transaction and detect a specific type of relay attack involving malicious readers. The premise of our work is a current technological advancement that can enable RFID tags with low-cost location (GPS) sensing capabilities. Unlike prior research on this subject, our defenses do not rely on auxiliary devices or require any explicit user involvement. Credit card fraud is a common problem in today's world. Financial institutions have registered major loses till today due to users being exposed of their credit card information. Shoulder-surfing or observation attacks, including card skimming and video recording with hidden cameras while users perform PIN-based authentication at ATM terminals is one of the common threats for common users. Researchers have struggled to come up with secure solutions for secure PIN authentication. However, modern day ubiquitous wearable devices, such as the Google Glass have presented us with newer opportunities in this research area. In this paper, we propose Secure-PIN-Authentication-as-a-Service (SEPIA), a secure obfuscated PIN authentication protocol for ATM and other point-of-service terminals using cloud-connected personal mobile and wearable devices. Our approach protects the user from shoulder-surfers and partial observation attacks, and is also resistant to relay, replay, and intermediate transaction attacks. A SEPIA user utilizes a Google Glass or a mobile device for scanning a QR code on the terminal screen to prove co-location to the cloud-based server and obtain a secure PIN template for point-of-service authentication. SEPIA ensures minimal task overhead on the user's device with maximal computation offloaded to the cloud. We have implemented a proof-of-concept prototype to perform experimental analysis and a usability study for the SEPIA architecture. 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 . A perpetual calendar that has a flat frame with two cavities. When cards are arranged in each cavity of the frame it will display a specific month. To display a particular number of days in a month, select the proper 31 day card and arrange the vertical slits on the card with the horizontal slit on the frame to obtain 28, 29, 30 or 31 days. To display a particular month choose the name of the month card to be viewed and insert it in the position of the frame provided for it. The few cards that are not used will remain in the frame cavities behind the month being displayed. This perpetual calendar can also display three consecutive months at one time. 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. Side-channel attacks are an increasingly important concern for the security of cryptographic embedded devices, such as the SIM cards used in mobile phones. Previous works have exhibited such attacks against implementations of the 2G GSM algorithms COMP-128, A5. In this paper, we show that they remain an important issue for USIM cards implementing the AES-based MILENAGE algorithm used in 3G 4G communications. In particular, we analyze instances of cards from a variety of operators and manufacturers, and describe successful Differential Power Analysis attacks that recover encryption keys and other secrets needed to clone the USIM cards within a few minutes. Further, we discuss the impact of the operator-defined secret parameters in MILENAGE on the difficulty to perform Differential Power Analysis, and show that they do not improve implementation security. Our results back up the observation that physical security issues raise long-term challenges that should be solved early in the development of cryptographic implementations, with adequatei?źcountermeasures. 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. 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. We live life in the network. When we wake up in the morning, we check our e-mail, make a quick phone call, walk outside (our movements captured by a high definition video camera), get on the bus (swiping our RFID mass transit cards) or drive (using a transponder to zip through the tolls). We arrive at the airport, making sure to purchase a sandwich with a credit card before boarding the plane, and check our BlackBerries shortly before takeoff. Or we visit the doctor or the car mechanic, generating digital records of what our medical or automotive problems are. We post blog entries confiding to the world our thoughts and feelings, or maintain personal social network profiles revealing our friendships and our tastes. Each of these transactions leaves digital breadcrumbs which, when pulled together, offer increasingly comprehensive pictures of both individuals and groups, with the potential of transforming our understanding of our lives, organizations, and societies in a fashion that was barely conceivable just a few years ago. Phishing is increasing dramatically with the development of modern technologies and the global worldwide computer networks. This results in the loss of customer’s confidence in e-commerce and online banking, financial damages, and identity theft. Phishing is fraudulent effort aims to acquire sensitive information from users such as credit card credentials, and social security number. In this article, we propose a model for predicting phishing attacks based on Artificial Neural Network (ANN). A Feed Forward Neural Network trained by Back Propagation algorithm is developed to classify websites as phishing or legitimate. The suggested model shows high acceptance ability for noisy data, fault tolerance and high prediction accuracy with respect to false positive and false negative rates. 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. 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. 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. The amount of contextual data collected, stored, mined, and shared is increasing exponentially. Street cameras, credit card transactions, chat and Twitter logs, e-mail, web site visits, phone logs and recordings, social networking sites, all are examples of data that persists in a manner not under individual control, leading some to declare the death of privacy. We argue here that the ability to generate convincing fake contextual data can be a basic tool in the fight to preserve privacy. One use for the technology is for an individual to make his actual data indistinguishable amongst a pile of false data. In this paper we consider two examples of contextual data, search engine query data and location data. We describe the current state of faking these types of data and our own efforts in this direction. 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. Consider three players Alice, Bob and Cath who hold a, b and c cards, respectively, from a deck of d = a+b+c cards. The cards are all different and players only know their own cards. Suppose Alice and Bob wish to communicate their cards to each other without Cath learning whether Alice or Bob holds a specific card. Considering the cards as consecutive natural numbers 0, 1, . . . , we investigate general conditions for when Alice or Bob can safely announce the sum of the cards they hold modulo an appropriately chosen integer. We demonstrate that this holds whenever a, b > 2 and c = 1. Because Cath holds a single card, this also implies that Alice and Bob will learn the card deal from the other player’s announcement. We consider the generic problem of Secure Aggregation of Distributed Information (SADI), where several agents acting as a team have information distributed amongst them, modelled by means of a publicly known deck of cards distributed amongst the agents, so that each of them knows only her cards. The agents have to exchange and aggregate the information about how the cards are distributed amongst them by means of public announcements over insecure communication channels, intercepted by an adversary "eavesdropper", in such a way that the adversary does not learn who holds any of the cards. We present a combinatorial construction of protocols that provides a direct solution of a class of SADI problems and develop a technique of iterated reduction of SADI problems to smaller ones which are eventually solvable directly. We show that our methods provide a solution to a large class of SADI problems, including all SADI problems with sufficiently large size and sufficiently balanced card distributions.