Phishing Attack Detection Using Taxonomy Model

Abstract

The objective of this paper is to detect phishing threats by using our proposed threat taxonomy model. To propose this threat taxonomy model, we have derived four different equations to calculate the predicted rate of phishing threat parameters that are used for phishing attacks. We have collected the information on phishing attacks by applying various methodologies, building an intellectual data set and experimenting on these data sets. We have done the experiment on the basis of our collected data sets and putting the values in four different equations which gave us the predicted rates of a phishing threat parameter such as; method, origin, component and target in respect of predicted number of threats. Experimenting on the intellectual data set, we got numerical results which are represented graphically. Finally, we got a phishing threat taxonomy model which demonstrates in a tabular form. The results show that even if some of the phishing threats’ methods, components and origins are different, the website can still be phished and forged, and users should be aware while dealing with it. Our proposed model showed a high heuristic accuracy for detecting the rate (%) of phishing attack when we applied our phishing detector software.

Keywords: Phishing Attack; Phishing Origin; Malware Attack; Phishing Target; Taxonomy Model; Phishing Attack Detection

Australian Academy of Business and Economics Review, vol 2, issue 1, January 2016, pp 22-39