Phishing Website Detection using Neural Network and PCA based on Feature Selection
Deyanara Tuapattinaya1, Antoni Wibowo2

1Deyanara Tuapattinaya*, Computer Science Department, Bina Nusantara University, Jakarta, Indonesia.
2Antoni Wibowo, Computer Science Department, Bina Nusantara University, Jakarta, Indonesia.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 30, 2020. | PP: 1150-1152 | Volume-8 Issue-6, March 2020. | Retrieval Number: D4532118419/2020©BEIESP | DOI: 10.35940/ijrte.D4532.038620

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Abstract: Phishing is a criminal activity that tries to steal user account password or other confidential information by tricking user into believing they are on the actual website. In order to phishing, they must get user to go from an email to a website. User can also land on phishing site by mistyping a URL (web address). However, the numbers of phishing attacks have been growing and need the protection technique. Neural network and Principal Component Analysis (PCA) can be combined to detect phishing website. This study uses back-propagation algorithm based on neural network method and PCA based on feature selection to reduce large attributes into small attributes. Neural network without using PCA will be compared with neural network using PCA. The result shows that neural network using PCA has better accuracy in 55.67% and neural network without using PCA only reaches 54.43% accuracy. However neural network without using PCA has faster computing time than neural network using PCA. This study can be used as a phishing protection technique.
Keywords: Back-Propagation, Neural Network, Phishing Website, Principal Component Analysis (PCA).
Scope of the Article: Underwater sensor networks.