Data Mining for Secure Online Payment Transaction

2017 ◽  
pp. 62-89
Author(s):  
Masoumeh Zareapoor ◽  
Pourya Shamsolmoali ◽  
M. Afshar Alam
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Detection Rate ◽  
Detection System ◽  
Fraud Detection ◽  
Holistic Approach ◽  
False Alarms ◽  
Detection Systems ◽  
Online Payment ◽  
Proposed Model

The fraud detection method requires a holistic approach where the objective is to correctly classify the transactions as legitimate or fraudulent. The existing methods give importance to detect all fraudulent transactions since it results in money loss. For this most of the time, they have to compromise on some genuine transactions. Thus, the major issue that the credit card fraud detection systems face today is that a significant percentage of transactions labelled as fraudulent are in fact legitimate. These “false alarms” delay the transactions and creates inconvenience and dissatisfaction to the customer. Thus, the objective of this research is to develop an intelligent data mining based fraud detection system for secure online payment transaction system. The performance evaluation of the proposed model is done on real credit card dataset and it is found that the proposed model has high fraud detection rate and less false alarm rate than other state-of-the-art classifiers.

Data Mining for Secure Online Payment Transaction

2018 ◽  
pp. 286-312
Author(s):  
Masoumeh Zareapoor ◽  
Pourya Shamsolmoali ◽  
M. Afshar Alam
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Detection Rate ◽  
Detection System ◽  
Fraud Detection ◽  
Holistic Approach ◽  
False Alarms ◽  
Detection Systems ◽  
Online Payment ◽  
Proposed Model

The fraud detection method requires a holistic approach where the objective is to correctly classify the transactions as legitimate or fraudulent. The existing methods give importance to detect all fraudulent transactions since it results in money loss. For this most of the time, they have to compromise on some genuine transactions. Thus, the major issue that the credit card fraud detection systems face today is that a significant percentage of transactions labelled as fraudulent are in fact legitimate. These “false alarms” delay the transactions and creates inconvenience and dissatisfaction to the customer. Thus, the objective of this research is to develop an intelligent data mining based fraud detection system for secure online payment transaction system. The performance evaluation of the proposed model is done on real credit card dataset and it is found that the proposed model has high fraud detection rate and less false alarm rate than other state-of-the-art classifiers.

Data Mining for Fraud Detection System

2011 ◽  
pp. 411-416
Author(s):  
Roberto Marmo
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
User Behavior ◽  
Detection System ◽  
Fraud Detection ◽  
Modern Technology ◽  
Data Sets ◽  
Security Measures ◽  
Economic Activities ◽  
Huge Data

As a conseguence of expansion of modern technology, the number and scenario of fraud are increasing dramatically. Therefore, the reputation blemish and losses caused are primary motivations for technologies and methodologies for fraud detection that have been applied successfully in some economic activities. The detection involves monitoring the behavior of users based on huge data sets such as the logged data and user behavior. The aim of this contribution is to show some data mining techniques for fraud detection and prevention with applications in credit card and telecommunications, within a business of mining the data to achieve higher cost savings, and also in the interests of determining potential legal evidence. The problem is very difficult because fraudsters takes many different forms and are adaptive, so they will usually look for ways to avoid every security measures.

scholarly journals Credit Card Fraud Detection through Parenclitic Network Analysis

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Massimiliano Zanin ◽  
Miguel Romance ◽  
Santiago Moral ◽  
Regino Criado
Keyword(s):  
Data Mining ◽  
Complex Network ◽  
Credit Card ◽  
Reference Group ◽  
Detection System ◽  
Fraud Detection ◽  
Data Representation ◽  
Classification Algorithm ◽  
Data Set ◽  
Economic Implications

The detection of frauds in credit card transactions is a major topic in financial research, of profound economic implications. While this has hitherto been tackled through data analysis techniques, the resemblances between this and other problems, like the design of recommendation systems and of diagnostic/prognostic medical tools, suggest that a complex network approach may yield important benefits. In this paper we present a first hybrid data mining/complex network classification algorithm, able to detect illegal instances in a real card transaction data set. It is based on a recently proposed network reconstruction algorithm that allows creating representations of the deviation of one instance from a reference group. We show how the inclusion of features extracted from the network data representation improves the score obtained by a standard, neural network-based classification algorithm and additionally how this combined approach can outperform a commercial fraud detection system in specific operation niches. Beyond these specific results, this contribution represents a new example on how complex networks and data mining can be integrated as complementary tools, with the former providing a view to data beyond the capabilities of the latter.

Hybrid Intrusion Detection System Security Enrichment Using Classifier Ensemble

2020 ◽  
Vol 17 (1) ◽  
pp. 434-438
Author(s):  
D. Karthikeyan ◽  
V. Mohanraj ◽  
Y. Suresh ◽  
J. Senthilkumar
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Detection System ◽  
Classifier Ensemble ◽  
Intrusion Detection Systems ◽  
Detection Systems ◽  
Malicious Activity ◽  
Using Data

Intrusion Detection Systems (IDS) is a software or device used to monitor a system or network for malicious activity. Thus, effective intrusion detection of different attacks. Existing methods of studies prove value of data mining methods in Intrusion Detection Systems (IDS). We focus on improving intrusion detection rate of IDS using Data Mining techniques. We implements a new classifier ensemble based intrusion detection systems (CEBIDS) using hybird detection approaches. CEBIDS combines feature level and data level techniques in WEKA tool with KDD cup’99 dataset enhances detection rate in significant manner.

scholarly journals An Xgboost based system for financial fraud detection

2020 ◽  
Vol 214 ◽  
pp. 02042
Author(s):  
Shimin LEI ◽  
Ke XU ◽  
YiZhe HUANG ◽  
Xinye SHA
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Credit Card ◽  
Detection System ◽  
Fraud Detection ◽  
Financial Fraud ◽  
Credit Card Fraud

Credit card fraud leads to billions of losses in online transaction. Many corporations like Alibaba, Amazon and Paypal invest billions of dollars to build a safe transaction system. There are some studies in this area having tried to use machine learning or data mining to solve these problems. This paper proposed our fraud detection system for e- commerce merchant. Unlike many other works, this system combines manual and automatic classifications. This paper can inspire researchers and engineers to design and deploy online transaction systems.

scholarly journals Credit Card Fraud Detection in Card-Not-Present Transactions: Where to Invest?

2021 ◽  
Vol 11 (15) ◽  
pp. 6766
Author(s):  
Igor Mekterović ◽  
Mladen Karan ◽  
Damir Pintar ◽  
Ljiljana Brkić
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Production Systems ◽  
Detection System ◽  
Academic Research ◽  
Fraud Detection ◽  
Detection Problem ◽  
Real World Data ◽  
Credit Card Fraud ◽  
Dominant Type

Online shopping, already on a steady rise, was propelled even further with the advent of the COVID-19 pandemic. Of course, credit cards are a dominant way of doing business online. The credit card fraud detection problem has become relevant more than ever as the losses due to fraud accumulate. Most research on this topic takes an isolated, focused view of the problem, typically concentrating on tuning the data mining models. We noticed a significant gap between the academic research findings and the rightfully conservative businesses, which are careful when adopting new, especially black-box, models. In this paper, we took a broader perspective and considered this problem from both the academic and the business angle: we detected challenges in the fraud detection problem such as feature engineering and unbalanced datasets and distinguished between more and less lucrative areas to invest in when upgrading fraud detection systems. Our findings are based on the real-world data of CNP (card not present) fraud transactions, which are a dominant type of fraud transactions. Data were provided by our industrial partner, an international card-processing company. We tested different data mining models and approaches to the outlined challenges and compared them to their existing production systems to trace a cost-effective fraud detection system upgrade path.

scholarly journals Hybrid classification model to detect advanced intrusions using data mining techniques

2018 ◽  
Vol 7 (2.4) ◽  
pp. 10
Author(s):  
V Mala ◽  
K Meena
Keyword(s):  
Data Mining ◽  
Learning Algorithm ◽  
Detection System ◽  
Classification Model ◽  
Detection Methods ◽  
Data Mining Techniques ◽  
Detection Systems ◽  
Intruder Detection ◽  
Hybrid Classification ◽  
Using Data

Traditional signature based approach fails in detecting advanced malwares like stuxnet, flame, duqu etc. Signature based comparison and correlation are not up to the mark in detecting such attacks. Hence, there is crucial to detect these kinds of attacks as early as possible. In this research, a novel data mining based approach were applied to detect such attacks. The main innovation lies on Misuse signature detection systems based on supervised learning algorithm. In learning phase, labeled examples of network packets systems calls are (gave) provided, on or after which algorithm can learn about the attack which is fast and reliable to known. In order to detect advanced attacks, unsupervised learning methodologies were employed to detect the presence of zero day/ new attacks. The main objective is to review, different intruder detection methods. To study the role of Data Mining techniques used in intruder detection system. Hybrid –classification model is utilized to detect advanced attacks.

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