BINARY LOGISTIC REGRESSION UNTUK MENDETEKSI WEBSITE PHISING MENGGUNAKAN CORRELATION-BASED FEATURE SELECTION
Abstract
Internet provides the facility to find customers worldwide without limitation use e-commerce market is effective. As a result the number of customers that rely on the Internet in the purchase has increased dramatically. In the field of computer security, phishing is a criminal activity that is trying to get sensitive information illegally. Sensitive information could include usernames, passwords and credit card details. This study aims to select the features or attributes in order to obtain the most influential attributes in detecting phishing websites. Selection feature using the correlation-based feature selection. Some of the most important attributes will be selected using the CFS and is applied to the binary logistic regression algorithms. Based on the research results show that CFS is able to eliminate redundant attributes. The subset of attributes generated have this level of accuracy is not much different from the full attributes. This level of accuracy before the selection of attributes 93.99% and 93.20% after the selection attribute. Subsequent studies applying other methods of feature selection and compared the results with the study.
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