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. 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. 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. 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.