Internet banking fraud detection using prudent analysis

The threat posed by cybercrime to individuals, banks and other online financial service providers is real and serious. Through phishing, unsuspecting victims’ Internet banking usernames and passwords are stolen and their accounts robbed. In addressing this issue, commercial banks and other financial institutions use a generically similar approach in their Internet banking fraud detection systems. This common approach involves the use of a rule-based system combined with an Artificial Neural Network (ANN). The approach used by commercial banks has limitations that affect their efficiency in curbing new fraudulent transactions. Firstly, the banks’ security systems are focused on preventing unauthorized entry and have no way of conclusively detecting an imposter using stolen credentials. Also, updating these systems is slow and their maintenance is labour-intensive and ultimately costly to the business. A major limitation of these rule-bases is brittleness; an inability to recognise the limits of their knowledge. To address the limitations highlighted above, this thesis proposes, develops and evaluates a new system for use in Internet banking fraud detection using Prudence Analysis, a technique through which a system can detect when its knowledge is insufficient for a given case. Specifically, the thesis proposes the following contributions:Doctor of Philosoph

Prudent fraud detection in internet banking

Most commercial Fraud Detection components of Internet banking systems use some kind of hybrid setup usually comprising a Rule-Base and an Artificial Neural Network. Such rule bases have been criticised for a lack of innovation in their approach to Knowledge Acquisition and maintenance. Furthermore, the systems are brittle; they have no way of knowing when a previously unseen set of fraud patterns is beyond their current knowledge. This limitation may have far reaching consequences in an online banking system. This paper presents a viable alternative to brittleness in Knowledge Based Systems; a potential milestone in the rapid detection of unique and novel fraud patterns in Internet banking. The experiments conducted with real online banking transaction log files suggest that Prudent based fraud detection may be a worthy alternative in online banking. © 2012 IEEE

ACOSTA, NIEVES [Material gráfico]

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte. Subdirección General de Coordinación Bibliotecaria, 201

Correction to 'Bifunctional fluorescent probes for detection of amyloid aggregates and reactive oxygen species'.

[This corrects the article DOI: 10.1098/rsos.171399.]

An Empırıcal Investıgatıon On Export-Led Growth Hypothesıs: A Case Study On Sıerra Leone

Bu çalışmada ihracata dayalı büyüme hipotezi 1967 ile 2014 arası yıllık veriler ile Sierra Leone için araştırılmıştır. Bu amaçla geliştirilen modelde kullanılan ihracat ve büyüme değişkenleri için önce Dickey-Fuller (ADF) ve Philips Perron (1988) durağanlık testleri yapılmıştır. Daha sonra ihracat ve ekonomik büyüme arasındaki uzun dönem ve nedensellik ilişkisini bulmak için Johansen kointegrasyon ve Granger Nedensellik testleri uygulanmıştır. Bulunan ampirik sonuçlar göstermiştir ki Sierra Leone için iktisadi büyümeden ihracata doğru tek yönlü bir nedensellik vardır. İhracata dayalı büyüme hipotezi bu ülke için geçerli değildirThis paper empirically investigates the export-led growth hypothesis for Sierra Leone using annual data for the period 1967 to 2014The first step in the empirical estimation is the univariate characteristics which show whether the variables are stationary or non-stationary. The paper uses both the Augmented Dickey-Fuller (ADF) and the Philips Perron (1988) statistics to test the stationarity or nonstationarity of the variables. Johansen co-integration and Granger Causality tests are employed to go beyond the traditional time series studies by examining empirically the long-run relationship and causality between exports and economic growth respectively. The study finds that in Sierra Leone growth in economic activities led to export. This means that the economic policy applicable to Sierra Leone is the Growth-led Export Hypothesis (GLEH). Therefore, the export-led growth hypothesis (ELGH) is not valid in the case of Sierra Leone rather there is growth leading expor

Ways of reducing accidents on South African roads

Paper presented at the 25th Annual Southern African Transport Conference 10 - 13 July 2006 "2010: Will transport infrastructure and systems be ready?", CSIR International Convention Centre, Pretoria, South Africa.This paper was transferred from the original CD ROM created for this conference. The material on the CD ROM was published using Adobe Acrobat technology. The original CD ROM was produced by Document Transformation Technologies Postal Address: PO Box 560 Irene 0062 South Africa. Tel.: +27 12 667 2074 Fax: +27 12 667 2766 E-mail: [email protected] URL: http://www.doctech.co.z

Video games' educational evaluation model based on BP neural network

Evaluating the education value of video games is a complex task. Based on the educational evaluation system for video games, we establish a corresponding automated evaluation model using the Back-propagation (BP) neural network technology. After training with the expert knowledge, the model not only has the experience of experts, but also has the computational capacity to evaluate new cases. © 2011 IEEE

Parameter optimization for Support Vector Machine Classifier with IO-GA

The Support Vector Machine method has a good learning and generalization ability. Unfortunately, there are no comprehensive theories to guide the parameter selection of the SVM, which largely limits its application. In order to get the optimal parameters automatically, researchers have tried a variety of methods. Using genetic algorithms to optimize parameters of an SVM Classifier has become one of the favorite methods in recent years. In this paper, we explain how the Standard Genetic Algorithm (SGA) causes the problem of premature convergence and limits the accuracy of the SVM. We also put forward a new genetic algorithm with improved genetic operators (IO-GA) to optimize the SVM classifier's parameters. Experimental results show that the parameters obtained by this method can greatly improve the classification performance of SVM. We therefore conclude that this method is effective. © 2011 IEEE

Rapid anomaly detection using integrated prudence analysis (IPA)

Integrated Prudence Analysis has been proposed as a method to maximize the accuracy of rule based systems. The paper presents evaluation results of the three Prudence methods on public datasets which demonstrate that combining attribute-based and structural Prudence produces a net improvement in Prudence Accuracy

RM and RDM, a preliminary evaluation of two prudent RDR techniques

Rated Multiple Classification Ripple Down Rules (RM) and Ripple Down Models (RDM) are two of the successful prudent RDR approaches published. To date, there has not been a published, dedicated comparison of the two. This paper presents a systematic preliminary evaluation and analysis of the two techniques. The tests and results reported in this paper are the first phase of direct evaluations of RM and RDM against each other