A Smart Methodology for Analyzing Secure E-Banking and E-Commerce Websites

Acquiring sensitive information from the user in some malicious web pages which looks like the legitimate webpage and they do a kind of criminal activity that is known as phishing in the electronic world. An attacker can use this kind of phishing or fraud by using such websites, which is a severe risk to web users for their personal and confidential information. So, in the field of e-banking and e-commerce, this act makes a threat for all webpage users. In this paper mainly discerning the different features of legitimate, suspicious and phishing websites. These features are fed to the machine learning algorithms which are built-in WEKA are used for comparison and to check the accuracy of the algorithm. Algorithms used in this comparison are J48, Naive Bayes, random forest and Logistic Model Tree (LMT) are used and them accurately to predict the website legitimacy is calculated. Also, the best algorithm among different algorithms can be selected. In this paper, we will compare the results in the two ways. Firstly, we find the best algorithm by using the comparison of the different attributes like Correctly Classified Instances, Incorrectly Classified Instances, Mean absolute error and kappa statistics. Secondly, the accuracy of these algorithms will analyze with different parameters like TP Rate, FP Rate, Precision, Recall, F-Measure, MCC, ROC Area and PRC Area that is visualized in the bar chart. The selected algorithm makes the website analyzing process automated. Before making payment on any e-commerce website, this prediction model can be used for determining the legitimacy of that website.