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.
Reasons for price differences include the types of cards and countries of origin, as already mentioned, in addition to the rarity and the quantity of the products to be purchased @cite_24 . Discounts on purchases of large card quantities lower the price per item. Furthermore, cards with more personal information available, with high balances and extended expiration dates and freshly acquired cards tend to be more expensive @cite_27 .
@cite_24 @cite_27
2007118926 2495607404
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.