Rule-Based Credit Card Fraud Detection Using User’s Keystroke Behavior

Abstract

In the digital era, security issue during online shopping is one of the prominent areas of the research for both the sides users and a businessman. In the current scenario, text-based authentication through one-time password is not reliable and also not capable of securing user’s transactional details as it can easily be hacked by the hackers through remote surfing of hand-held devices, hidden cameras, credit and debit card cloning, etc. In the present work, keystroke dynamics is applied for minimizing the credit card fraud during online transactions by the users. The identity verification verifies using rule-based expert system, and user’s behaviors are also calculated using keystroke dynamics when the user is typing transaction pin. The user’s behavior such as typing pattern, time, and speed of a particular user can be recorded using the keyboard without the knowledge of the user and the service provider need not install any hardware devices; therefore, the proposed system is very cost effective, and it improves the security aspect for the online transaction. The proposed experimental model proves that it provides higher security and minimizing the credit card frauds while performing online transactions.