A Novel Approach to Credit Card Security with Generative Adversarial Networks and Security Assessment

Abstract

With the rapid development of Internet technology, many industries have begun digital transformation. However, while bringing convenience to users, the Internet has also become a hotbed for criminals to commit fraud. On the one hand, a large number of users on the Internet more or less left data, criminals can use this information to practice accurate fraud users, improve the success rate of fraud; On the other hand, online financial transactions such as banks and e-commerce also provide more ways for criminals to commit fraud. Therefore, all kinds of fraud methods emerge in an endless flow, through the telephone, information, fishing and other means of fraud, not only to bring hundreds of millions of losses to the society every year, but also to the safety of people's lives have a huge threat. Monitoring and preventing online fraud is an important part of the cybersecurity industry. For known network fraud, based on the phishing site domain name, the account number and mobile phone number that send fraudulent information, simple and effective supervision and defense can be carried out through the blacklist. However, it is difficult for traditional means to effectively defend against undocumented fraud. With the development of machine learning technology, it is the main research direction of fraud detection methods to discover the information sources and characteristics of the information content through machine learning technology, and make real-time and continuous accurate judgments. This paper realizes credit card fraud detection by generating adversarial network technology, so as to prevent network security risks.