Abstract: Acquiring sensitive info from the user in some malicious sites that looks like the legitimate webpage and that they do a sort of criminal activity that's referred to as phishing within the electronic world. associate degree assailant will use this type of phishing or fraud by using such websites, that could be a severe risk to internet users for his or her personal and confidential information. So, within the field of e-banking and e-commerce, this act makes a threat for all webpage users. during this paper in the main discerning the various options of legitimate, suspicious and phishing websites. These options are fed to the machine learning algorithms that are constitutional hence are used for comparison and to ascertain the accuracy of the algorithmic rule. Algorithms utilized in this comparison are J48, Naïve Bayes, random forest and supply Model Tree (LMT) are used and them accurately to predict the web site legitimacy is calculated. Also, the most effective algorithmic rule among completely different algorithms will be selected. during this paper, we'll compare the ends up in the 2 ways that. Firstly, we discover the best algorithmic rule by mistreatment the comparison of the various attributes like properly Classified Instances, Incorrectly Classified Instances, Mean absolute error and letter of the alphabet statistics. Secondly, the accuracy of those algorithms can analyse with completely different parameters like TP Rate, FP Rate, Precision, Recall, F-Measure, MCC, mythical creature space and People's Republic of China space that's visualized within the chart. the chosen algorithmic rule makes the web site analysing method automated. Before creating payment on any e-commerce web site, this prediction model can be used for determinative the legitimacy of that web site.

PDF | DOI: 10.17148/IJARCCE.2021.10118

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