WEBSITE PHISING DETECTION APPLICATION USING SUPPORT VECTOR MACHINE (SVM)

Abstract

Phishing is an act to get someone's important information in the form of usernames, passwords, and other sensitive information by providing fake websites that are similar to the original. Phishing (fishing for important information) is a form of criminal act that intends to obtain confidential information from someone, such as usernames, passwords and credit cards, by impersonating a trusted person or business in an official electronic communication, such as electronic mail or instant messages. Along with the development of the use of electronic media, which is followed by the increase in cyber crime, such as this phishing attack. Therefore, to minimize phishing attacks, a system is needed that can detect these attacks. Machine Learning is one method that can be used to create a system that can detect phishing. The data used in this research is 11055 website data, which is divided into two classes, namely "legitimate" and "phishing". This data is then divided using 10-fold cross validation. While the algorithm used is the Support Vector Machine (SVM) algorithm which is compared with the decision tree and k-nearest neighbor algorithms by optimizing the parameters for each algorithm. From the test results in this study, the best system accuracy was 85.71% using SVM kernel polynomial with values of degree 9 and C 2.5.