Alamgir Hossain

Detecting and identifying any phishing websites in real-time, particularly for e-banking is really a complex and dynamic problem involving many factors and criteria. Because of the subjective considerations and the ambiguities involved in the detection, Fuzzy Data Mining (DM) Techniques can be an effective tool in assessing and identifying phishing websites for e-banking since it offers a more natural way of dealing with quality factors rather than exact values. In this paper, we present novel approach to overcome the ‘fuzziness’ in the e-banking phishing website assessment and propose an intelligent resilient and effective model for detecting e-banking phishing websites. The proposed model is based on Fuzzy logic (FL) combined with Data Mining algorithms to characterize the e-banking phishing website factors and to investigate its techniques by classifying there phishing types and defining six e-banking phishing website attack criteria’s with a layer structure. The proposed e-banking phishing website model showed the significance importance of the phishing website two criteria’s (URL & Domain Identity) and (Security & Encryption) in the final phishing detection rate result, taking into consideration its characteristic association and relationship with each others as showed from the fuzzy data mining classification and association rule algorithms. Our phishing model also showed the insignificant trivial influence of the (Page Style & Content) criteria along with (Social Human Factor) criteria in the phishing detection final rate result.

A classification paradigm is a data mining framework containing all the concepts extracted from the training dataset to differentiate one class from other classes existed in data. The primary goal of the classification frameworks is to provide a better result in terms of accuracy. However, in most of the cases we can not get better accuracy particularly for huge dataset and dataset with several groups of data . When a classification framework considers whole dataset for training then the algorithm may become unusuable because dataset consisits of several group of data. The alternative way of making classification useable is to identify a similar group of data from the whole training data set and then training each group of similar data. In our paper, we first split the training data using kmeans clustering and then train each group with Naive Bayes Classification algorithm. In addition, we saved each model to classify sample or unknown or test data. For unknown data, we classify with the best match group/model and attain higher accuracy rate than the conventional Naive Bayes classifier.

This paper presents a comprehensive literature survey and analysis on Phishing, Vishing and Smishing to exploit the knowledge in implementing an intelligent tool for detection and protection. This is a new social engineering problem which makes our day to day life vulnerable and difficult. This investigation particularly focuses on phishing through email as it has more serious consequences directly related to financial transactions in comparison to the other methods. It is worth mentioning that securing the enormous amount of online transactions is very challenging since several methods are invented daily to breach individual privacy in order to steal their credentials. The cost of these types of attacks exceeds millions of dollars annually. Many tools are proposed to solve this problem; unfortunately, the dilemma still exists. This paper proposes a methodology to develop an intelligent tool and awareness security program to address the risk of this problem.