Credit Card Fraud Detection using Deep Learning based on Auto-Encoder and Restricted Boltzmann Machine
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2018, Vol 9, Issue 1
Abstract
Frauds have no constant patterns. They always change their behavior; so, we need to use an unsupervised learning. Fraudsters learn about new technology that allows them to execute frauds through online transactions. Fraudsters assume the regular behavior of consumers, and fraud patterns change fast. So, fraud detection systems need to detect online transactions by using unsupervised learning, because some fraudsters commit frauds once through online mediums and then switch to other techniques. This paper aims to 1) focus on fraud cases that cannot be detected based on previous history or supervised learning, 2) create a model of deep Auto-encoder and restricted Boltzmann machine (RBM) that can reconstruct normal transactions to find anomalies from normal patterns. The proposed deep learning based on auto-encoder (AE) is an unsupervised learning algorithm that applies backpropagation by setting the inputs equal to the outputs. The RBM has two layers, the input layer (visible) and hidden layer. In this research, we use the Tensorflow library from Google to implement AE, RBM, and H2O by using deep learning. The results show the mean squared error, root mean squared error, and area under curve.
Authors and Affiliations
Apapan Pumsirirat, Liu Yan
Keywords
Related Articles
- EP ID EP261407
- DOI 10.14569/IJACSA.2018.090103
- Views 84
- Downloads 0
Triple SV: A Bit Level Symmetric Block-Cipher Having High Avalanche Effect
The prolific growth of network communication system entails high risk of breach in information security. This substantiates the need for higher security for electronic information. Cryptography is one of the ways to secu...
Improved Sliding Mode Nonlinear Extended State Observer based Active Disturbance Rejection Control for Uncertain Systems with Unknown Total Disturbance
This paper presents a new strategy for the active disturbance rejection control (ADRC) of a general uncertain system with unknown bounded disturbance based on a nonlinear sliding mode extended state observer (SMESO). Fir...
Comparison of Inter-and Intra-Subject Variability of P300 Spelling Dictionary in EEG Compressed Sensing
In this paper, we propose a new compression method for electroencephalographic signals based on the concept of compressed sensing (CS) for the P300 detection spelling paradigm. The method uses a universal mega-dictionary...
An Intelligent Security Approach using Game Theory to Detect DoS Attacks In IoT
The Internet of Things (IoT) is a new concept in the world of Information and Communication Technology (ICT). The structure of this global network is highly interconnected and presents a new category of challenges from t...
Diagnosis of Wind Energy System Faults Part I : Modeling of the Squirrel Cage Induction Generator
Generating electrical power from wind energy is becoming increasingly important throughout the world. This fast development has attracted many researchers and electrical engineers to work on this field. The authors devel...