A Study of Phishing Detection Using Associative Data Mining

Abstract

Hacking is an online fraud where by the criminal pretend to be someone else in order to obtain sensitive information like database information, admin username and password, credit card number, password for bank account, email eBay, PayPal etc. This paper explain that how the hackers hack the web pages and how to prevent themselves, the tricks and the methods the criminal explore to get their victim, it also describe how they are threat to E-business. Lastly it proffers solution how to avoid being hacked both by individual and corporate organization. Examples to minimize the threat of these problems are White List, Black List and the utilization of search methods. The Black List one of the popular and widely used technique into browsers, but they are not much more effective and unsure. Associative Classification (AC) is one of the techniques based on data mining used to find phishing websites with high purity. By using If-Then rules AC extracts classifiers with a large degree of guessing accuracy.AC method developed Multi-label Classifier based Associative Classification (MCAC) for the problem of website phishing and to find features that differentiate phishing websites from legitimate ones. In this paper, MCAC identify phishing websites with higher purity and MCAC originate new hidden rules that other algorithms are not able to find and this has improved its classifiers predictive performance.