Abstract:
"
Many internet facilities have fascinated users to migrate to online banking and grow
exponentially in the past few years. Payments done using credit cards are common and
are quite crucial in numerous countries, and also simultaneously, frauds are increasing.
Doing the transaction using a credit card becomes the norm with small businesses.
When considering both legitimate users and fraudsters use mobile transactions.
Therefore they become more exposed to large-scale systematic fraud. Credit card fraud
has a significant negative effect because the economic impact incurred affects all parties
involved.
Fraud can be identified by looking at previous transaction data and examining customer
purchasing patterns if any deviation in spending behaviour from established trends may
indicate a fraudulent transaction. Banks and credit card firms use several approaches to
detect fraud, such as rule-based expert system or machine learning techniques. When
considering the machine learning technique, supervised learning approaches are
commonly used. These supervised learning approaches for fraud detection, on the other
hand, have generally advanced with an assumption on that it is a benign environment.
There are no adversaries attempting to get through the fraud detection system.
This research approach for improve robustness in credit card fraud detection, built a
framework that can test with adversarial attacks environment and normal environment,
also implement the defence mechanism for against to adversarial attacks. This concept
using Europe and German credit card dataset were an experiment. These multiple
experiments showed proposed approach improved the precession, f1-score, and recall
than existing researches. This research considered fraudster’s potential reactions to
build a robust credit card fraud detection system using testing with different adversarial
attacks and implementing defence mechanism for attacks."
