سیستم تشخیص هوشمند فیشینگ برای بانکداری‌ الکترونیکی با استفاده از داده‌کاوی فازی

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 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 combined with data mining algorithms to characterize the e-banking phishing website factors and to investigate its techniques by classifying the phishing types and defining six e-banking phishing website attack criteria’s with a layer structure. Our experimental results showed the significance and importance of the e-banking phishing website criteria (URL & Domain Identity) represented by layer one and the various influence of the phishing characteristic on the final e-banking phishing website rate.

E-banking phishing websites are forged websites that are created by malicious people to mimic real e-banking websites. Most of these kinds of web pages have high visual similarities to scam their victims. Some of these web pages look exactly like the real ones. Unwary Internet users may be easily deceived by this kind of scam. Victims of e-banking phishing websites may expose their bank account, password, credit card number, or other important information to the phishing web page owners. The impact is the breach of information security through the compromise of confidential data, and the victims may finally suffer losses of money or other kinds. Phishing is a relatively new Internet crime in comparison with other forms, e.g., virus and hacking. More and more phishing web pages have been found in recent years in an accelerative way (Fu, Wenyin, & Deng, 2006). The word phishing from the phrase “website phishing” is a variation on the word “fishing”. The idea is that bait is thrown out with the hopes that a user will grab it and bite into it just like the fish. In most cases, bait is either an e-mail or an instant messaging site, which will take the user to hostile phishing websites (James, 2006). E-banking phishing website is a very complex issue to understand and to analyze, since it is joining technical and social problem with each other for which there is no known single silver bullet to entirely solve it. The motivation behind this study is to create a resilient and effective method that uses fuzzy data mining algorithms and tools to detect e-banking phishing websites in an automated manner. DM approaches such as neural networks, rule induction, and decision trees can be a useful addition to the fuzzy logic model. It can deliver answers to business questions that traditionally were too time-consuming to resolve such as, “Which are most important e-banking phishing website characteristic indicators and why?” by analyzing massive databases and historical data for training purposes. The paper is organized as follows: Section 2 presents the literature review and related work, Section 3 shows the theory and methodology of the proposed fuzzy based data mining approach for the phishing website risk assessment model. Section 4 introduces the system design and implementation with the overall fuzzy data mining inference rules. Section 5 reveals the experiments and results of the fuzzy data mining e-banking phishing website risk assessment model and then conclusions and future work are given in Section 6

The fuzzy data mining e-banking phishing website model showed significance and importance of the e-banking phishing website criteria (URL & Domain Identity) represented by layer 1. It also showed that even if some of the e-banking phishing website characteristics or layers are not very clear or not definite, the website can still be phishy especially when other phishing characteristics or layers are obvious and clear. On the other hand, even if some of the e-banking phishing website characteristics or layers are noticed or observed, it does not mean at all that the website is phishy, but it can be safe and secured especially when other phishing characteristics or layers are not noticeable, visible, or detectable. Our first goal was to determine whether we could find any golden nuggets in the e-banking phishing website archive data using classification algorithms. In this, major rules discovered were inserted into the fuzzy rule engine to help giving exact phishing rate output. A major issue in using data mining algorithms is the preparation of the feature sets to be used. Finding the “right” feature set is a difficult problem and requires some intuition regarding the goal of data mining exercise. We are not convinced that we have used the best feature sets and we think that there is more work to be done in this area. Moreover, there are number of emerging technologies that could greatly assist phishing classification that we have not considered. However, we believe that using features such as those presented here can significantly help with detecting this class of e-banking phishing websites. The classification approaches are promising. The training and classification experiments have proven that it is possible to improve the categorization process. The progress of detecting e-banking phishing websites is really very interesting with a never ending possibility of algorithms variations when it is combined with each other.