Tutorial and Critical Analysis of Phishing Websites Methods

The Internet has become an essential component of our everyday social and financial activities. Internet is not important for individual users only but also for organizations, because organizations that offer online trading can achieve a competitive edge by serving worldwide clients. Internet facilitates reaching customers all over the globe without any market place restrictions and with effective use of e-commerce. As a result, the number of customers who rely on the Internet to perform procurements is increasing dramatically. Hundreds of millions of dollars are transferred through the Internet every day. This amount of money was tempting the fraudsters to carry out their fraudulent operations. Hence, Internet users may be vulnerable to different types of web threats, which may cause financial damages, identity theft, loss of private information, brand reputation damage and loss of customers’ confidence in e-commerce and online banking. Therefore, suitability of the Internet for commercial transactions becomes doubtful. Phishing is considered a form of web threats that is defined as the art of impersonating a website of an honest enterprise aiming to obtain user’s confidential credentials such as usernames, passwords and social security numbers. In this article, the phishing phenomena will be discussed in detail. In addition, we present a survey of the state of the art research on such attack. Moreover, we aim to recognize the up-to-date developments in phishing and its precautionary measures and provide a comprehensive study and evaluation of these researches to realize the gap that is still predominating in this area. This research will mostly focus on the web based phishing detection methods rather than email based detection methods

A Comprehensive Survey of Data Mining-based Fraud Detection Research

This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the business context of mining the data to achieve higher cost savings, this research presents methods and techniques together with their problems. Compared to all related reviews on fraud detection, this survey covers much more technical articles and is the only one, to the best of our knowledge, which proposes alternative data and solutions from related domains.Comment: 14 page

Displacing big data: How criminals cheat the system

Abstract: Many technical approaches for detecting and preventing cy-bercrime utilise big data and machine learning, drawing upon knowledgeabout the behaviour of legitimate customers and indicators of cyber-crime. These include fraud detection systems, behavioural analysis, spamdetection, intrusion detection systems, anti-virus software, and denial ofservice attack protection. However, criminals have adapted their meth-ods in response to big data systems. We present case studies for a numberof different cybercrime types to highlight the methods used for cheatingsuch systems. We argue that big data solutions are not a silver bulletapproach to disrupting cybercrime, but rather represent a Red Queen'srace, requiring constant running to stay in one spot

Data Mining Tools and Techniques: a review

Data mining automates the detection of relevant patterns in a database, using defined approaches andalgorithms to look into current and historical data that can then be analyzed to predict future trends.Because data mining tools predict future trends and behaviors by reading through databases for hiddenpatterns, they allow organizations to make proactive, knowledge-driven decisions and answer questions thatwere previously too time-consuming to resolve. The data mining methods such as clustering, associationrules, sequential pattern, statistics analysis, characteristics rules and so on can be used to find out the usefulknowledge, enabling such data to become the real fortune of logistics companies and support theirdecisions and development. This paper introduces the significance use of data mining tools and techniquesin logistics management system, and its implications. Finally, it is pointed out that the data miningtechnology is becoming more and more powerful in logistics management.Keywords: Logistics management, Data Mining concepts, application areas, Tools and Technique

Análise do risco de inadimplência na utilização de cartões de crédito

ABSTRACT. This paper analyzes the risk of default in the use of credit cards generating probabilities of delay in payment with different variables such as age, gender, credit limit and annual income. The behavior of debtors who use credit cards is studied identifying changes in states of delay of risk levels. A multi-state model of Markov was used to perform the analysis. The study was applied to credit card usage records of individuals in 121 commercial and financial institutions. This research identifies the patterns of use by credit card customers and provides valuable inputs to help financial institutions understand the phenomenon of default risk.RESUMO. Este trabalho analisa o risco de inadimplência na utilização de cartões de crédito gerando probabilidades de atraso no pagamento com diferentes variáveis tais como idade, sexo, limite de crédito e rendimento anual. O comportamento dos devedores que utilizam cartões de crédito é estudado identificando alterações nos estados de atraso dos níveis de risco. Foi utilizado um modelo multiestado de Markov para realizar a análise. O estudo foi aplicado aos registos de utilização de cartões de crédito de indivíduos em 121 instituições comerciais e financeiras. Este estudo identifica os padrões de utilização pelos clientes de cartões de crédito e fornece dados valiosos para ajudar as instituições financeiras a compreender o fenómeno do risco de inadimplência