Improved SVM for Website Phishing Detection Through Recursive Feature Elimination

Abstract

Technology is developing faster every day, particularly in the information technology field. A website is one of the many information access points people use to do business activities, get information, and other purposes. Sophisticated websites are being developed and used, encouraging many naive individuals to commit crimes for financial gain. Phishing websites are a common method of using information technology to conduct fraud. One way to conduct phishing is by using the features on the website. One technique for identifying phishing websites is to use the Support Vector Machine (SVM) algorithm, which classifies websites based on features. However, the SVM algorithm is not able to detect many features so that the resulting accuracy and optimization level is also not good. Based on datasets, the SVM algorithm only gets around 60% to 70% accuracy. The use of Recursive Feature Elimination (RFE) feature selection is one way that can be done to cover the shortcomings of SVM. By eliminating features that irrelevant and redundance, RFE makes the SVM algorithm get a higher accuracy rate on the available dataset with an accuracy of 96.09%.