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Keyword: FraudAn Adaptive Heterogeneous Ensemble Learning Model for Credit Card Fraud Detection
The proliferation of internet economies has given the corporate world manifold advantages to businesses, as they can now incorporate the latest innovations into their operations, thereby enhancing ease of doing business. For instance, financial institutions have leveraged credit card usage on the aforesaid proliferation. However, this exposes clients to cybercrime, as fraudsters always find ways…
Read MoreFraud Detection Call Detail Record Using Machine Learning in Telecommunications Company
Fraud calls have a serious impact on telecommunications operator revenues. Fraud detection is very important because service providers can feel a significant loss of income. We conducted a fraud research case study on one of the operators that experienced fraud in 2009 and 2018. Call Detail Record (CDR) containing records of customer conversations such as…
Read MoreApproach to Combine an Ontology-Based on Payment System with Neural Network for Transaction Fraud Detection
Fraud, as regards means of payment, means the behavior of any legal or natural one that makes an abnormal or irregular use of a way of payment, elements of it or information contained therein, to improperly obtain an honest, service or enrichment, and or causing financial damage to the one that has distributed the means…
Read MoreAggrandized Random Forest to Detect the Credit Card Frauds
From the collection of supervised machine learning technique, an ensemble procedure is used in Random Forest. In the arena of Data mining, there is an excellent claim for machine learning techniques. Random Forest has tremendous latent of becoming a widespread technique for forthcoming classifiers as its performance has been found analogous with ensemble techniques bagging…
Read MoreBiometric System Vulnerabilities: A Typology of Metadata
This study presents a root cause analysis of biometric vulnerabilities and provides a comprehensive typology of metadata in biometric adaptation. Although they are more reliable and secure than traditional authentication methods, biometric techniques are subject to vulnerabilities that pose challenges. Faced with the proliferation of cases of identity theft and fraud, biometrics is increasingly used…
Read MoreEKMC: Ensemble of kNN using MetaCost for Efficient Anomaly Detection
Anomaly detection aims at identification of suspicious items, observations or events by differing from most of the data. Intrusion Detection, Fault Detection, and Fraud Detection are some of the various applications of Anomaly Detection. The Machine learning classifier algorithms used in these applications would greatly affect the overall efficiency. This work is an extension of…
Read MoreSmart Meter Data Analysis for Electricity Theft Detection using Neural Networks
The major problem in electric utility is Electrical Theft, which is harmful to electric power suppliers and causes economic loss. Detecting and controlling electrical theft is a challenging task that involves several aspects like economic, social, regional, managerial, political, infrastructural, literacy rate, etc. Numerous methods were proposed formerly for detecting electricity theft. However, the previous…
Read MoreBehavioral Analysis of Bitcoin Users on Illegal Transactions
Bitcoin is a popular crypto currency that is used as a mode of investment and a medium for trading goods and services. Anonymity, security and decentralization are significant features of Bitcoin. This creates several opportunities for criminals to involve in illegal and fraudulent activities. This research study aimed to automate the process of gaining the…
Read MoreWeb Authentication: no Password; Listen and Touch
Just as electricity has an essential role in our lives, the internet network and especially web services have become of vital importance nowadays. Without security service layers, apparently small things like checking a child’s school schedule on web may turn the daily routine into a nightmare. Web services users are still required to use many…
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