Detection of Phishing Attacks using Radial Basis Function Network Trained for Categorical Attributes
S. Priya, Subramanian Selvakumar, R. Leela Velusamy · 2020
Phishing attacks have become the most common cyber-security threat faced by the online users, in which the online credentials of the user are vulnerable to commit the financial crimes. The loss of money and loss of reputation of organization are the notable issues of this attack. Although several anti-phishing strategies have been proposed in the literature, this crucial issue still solicits the detection technique that favors high detection accuracy and low false alarm in the online community. Hence, in this work the Radial Basis Function (RBF) network with its enhanced hyper parameters is proposed for predicting the phishing websites. The proposed detection model utilizes the unsupervised learning for estimating the RBF kernels as well as the spread constant and obtained the categorical RBF for detecting the phishing websites. The proposed approach was tested on benchmark phishing datasets and the performance was compared with the existing neural network classifiers to prove its effectiveness.