scholarly journals Credit Card Fraud Detection in Payment Using Machine Learning Classifiers

2020 ◽  
Vol 8 (4) ◽  
Author(s):  
Maad M. Mijwil ◽  
Israa Ezzat Salem
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Credit Card ◽  
Fraud Detection ◽  
Classification Problem ◽  
Recall Rate ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Precision Recall Curve ◽  
Bagging Ensemble

The fraud detection in payment is a classification problem that aims to identify fraudulent transactions based individually on the information it contains and on the basis that a fraudster's behaviour patterns differ significantly from that of the actual customer. In this context, the authors propose to implement machine learning classifiers (Naïve Bayes, C4.5 decision trees, and Bagging Ensemble Learner) to predict the outcome of regular transactions and fraudulent transactions. The performance of these classifiers is judged by the following ways: precision, recall rate, and precision-recall curve (PRC) area rate. The dataset includes more than 297K transactions via credit cards in September 2013 and November 2017 that have been collected from Kaggle platform, of which 3293 are frauds. The performance PRC ratio of machine learning classifiers is between 99.9% and 100%, which confirms that these classifiers are very good at identifying binary classes 0 in the dataset. The results of the tests have proved that the best classifier is C4.5 decision trees. This classifier has the best accuracy of 94.12% in prediction of fraudulent transactions.

scholarly journals Comparing Machine Learning Classifiers for Continuous Authentication on Mobile Devices by Keystroke Dynamics

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1622
Author(s):  
Luis de-Marcos ◽  
José-Javier Martínez-Herráiz ◽  
Javier Junquera-Sánchez ◽  
Carlos Cilleruelo ◽  
Carmen Pages-Arévalo
Keyword(s):  
Machine Learning ◽  
Ensemble Methods ◽  
Classification Problem ◽  
Probabilistic Methods ◽  
Mobile Environment ◽  
User Interactions ◽  
Machine Learning Classifiers ◽  
Continuous Authentication ◽  
Learning Classifiers ◽  
Ensemble Algorithms

Continuous authentication (CA) is the process to verify the user’s identity regularly without their active participation. CA is becoming increasingly important in the mobile environment in which traditional one-time authentication methods are susceptible to attacks, and devices can be subject to loss or theft. The existing literature reports CA approaches using various input data from typing events, sensors, gestures, or other user interactions. However, there is significant diversity in the methodology and systems used, to the point that studies differ significantly in the features used, data acquisition, extraction, training, and evaluation. It is, therefore, difficult to establish a reliable basis to compare CA methods. In this study, keystroke mechanics of the public HMOG dataset were used to train seven different machine learning classifiers, including ensemble methods (RFC, ETC, and GBC), instance-based (k-NN), hyperplane optimization (SVM), decision trees (CART), and probabilistic methods (naïve Bayes). The results show that a small number of key events and measurements can be used to return predictions of user identity. Ensemble algorithms outperform others regarding the CA mobile keystroke classification problem, with GBC returning the best statistical results.

scholarly journals Machine Learning for Detecting Credit Card Frauds

2020 ◽  
Vol 8 (2S12) ◽  
pp. 16-23
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Data Science ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
System Administrator ◽  
The One ◽  
Local Outlier ◽  
Isolation Forest ◽  
Different Parts

Credit card frauds has been a threat that has evolved as a major source of loss for the financial sectors. It has been seen in the different parts of world causing loss of billions of dollars. It is also a area which needs attention from the researchers as the task of fraud detection can be automated using the different machine learning classifiers and data science. If the frauds model encounter the fraudulent transactions it will raise an alarm to the system administrator. The paper proposes a model which uses the machine learning classifiers to detect the fraudulent transactions. The classifiers used in the paper are SVM (Support Vectore Machine ), Isolation Forest and Local Outlier. The focus of the research is to detect the fraudulent transactions to 100% and also we emphasise on the fact that no normal transaction should be detected as fraud wrongly. The process starts with preprocessing the data and then the classifers are applied. The results from each classifers is evaluated to check the one with the better performance. The performance can be increased with use of deep learning algorithms but with the rise in expennses.

Toward False Event Detection and Quarry Blast versus Earthquake Discrimination in an Operational Setting Using Semiautomated Machine Learning

2021 ◽  
Author(s):  
Alexandra Renouard ◽  
Alessia Maggi ◽  
Marc Grunberg ◽  
Cécile Doubre ◽  
Clément Hibert
Keyword(s):  
Machine Learning ◽  
Expert Knowledge ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
High Signal ◽  
Small Magnitude ◽  
Magnitude Distribution ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Natural Seismicity

Abstract Small-magnitude earthquakes shed light on the spatial and magnitude distribution of natural seismicity, as well as its rate and occurrence, especially in stable continental regions where natural seismicity remains difficult to explain under slow strain-rate conditions. However, capturing them in catalogs is strongly hindered by signal-to-noise ratio issues, resulting in high rates of false and man-made events also being detected. Accurate and robust discrimination of these events is critical for optimally detecting small earthquakes. This requires uncovering recurrent salient features that can rapidly distinguish first false events from real events, then earthquakes from man-made events (mainly quarry blasts), despite high signal variability and noise content. In this study, we combined the complementary strengths of human and interpretable rule-based machine-learning algorithms for solving this classification problem. We used human expert knowledge to co-create two reliable machine-learning classifiers through human-assisted selection of classification features and review of events with uncertain classifier predictions. The two classifiers are integrated into the SeisComP3 operational monitoring system. The first one discards false events from the set of events obtained with a low short-term average/long-term average threshold; the second one labels the remaining events as either earthquakes or quarry blasts. When run in an operational setting, the first classifier correctly detected more than 99% of false events and just over 93% of earthquakes; the second classifier correctly labeled 95% of quarry blasts and 96% of earthquakes. After a manual review of the second classifier low-confidence outputs, the final catalog contained fewer than 2% of misclassified events. These results confirm that machine learning strengthens the quality of earthquake catalogs and that the performance of machine-learning classifiers can be improved through human expertise. Our study promotes a broader implication of hybrid intelligence monitoring within seismological observatories.

scholarly journals Comparison study of machine learning classifiers to detect anomalies

2020 ◽  
Vol 10 (5) ◽  
pp. 5445
Author(s):  
Nisha P Shetty ◽  
Jayashree Shetty ◽  
Rohil Narula ◽  
Kushagra Tandona
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Credit Card ◽  
Sensitive Information ◽  
Comparison Study ◽  
Intrusion Prevention ◽  
Detection Techniques ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Real Time Detection

In this era of Internet ensuring the confidentiality, authentication and integrity of any resource exchanged over the net is the imperative. Presence of intrusion prevention techniques like strong password, firewalls etc. are not sufficient to monitor such voluminous network traffic as they can be breached easily. Existing signature based detection techniques like antivirus only offers protection against known attacks whose signatures are stored in the database.Thus, the need for real-time detection of aberrations is observed. Existing signature based detection techniques like antivirus only offers protection against known attacks whose signatures are stored in the database. Machine learning classifiers are implemented here to learn how the values of various fields like source bytes, destination bytes etc. in a network packet decides if the packet is compromised or not . Finally the accuracy of their detection is compared to choose the best suited classifier for this purpose. The outcome thus produced may be useful to offer real time detection while exchanging sensitive information such as credit card details.

Testing Machine Learning Classifiers based on Compositional Metamorphic Relations

2020 ◽  
Vol 16 (1) ◽  
pp. 67
Author(s):  
Minghua Jia ◽  
Xiaodong Wang ◽  
Yue Xu ◽  
Zhanqi Cui ◽  
Ruilin Xie
Keyword(s):  
Machine Learning ◽  
Testing Machine ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

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