Anti-phishing model based on relative content mining
by Parvinder Singh; Bhawna Sharma
International Journal of Computational Vision and Robotics (IJCVR), Vol. 12, No. 1, 2022

Abstract: Phishing has attracted larger section of researchers and application developers not due to the rising instances of phishing attacks but also due to the sophisticated techniques that are being employed to execute on the attack. To address one of the diverse mechanisms of phishing attacks, the authors have proposed an anti-phishing model for detecting phishing URLs using relative content mining. The relative similarity calculation method uses a combination of cosine similarity and Jaccard similarity. Machine learning oriented feed forward back propagation neural networks (FFBPNN) in combination with support vector machine (SVM) algorithms are used as an anti-phishing technique. A hybrid training and classification algorithm using three kernels namely linear, polynomial and radial basis function (RBF) are implemented. The proposed approach provides best solution for the detection of the phisher in the cyber world. Multiple scenarios such as precision and accuracy are calculated to evaluate the proposed work. Precision of the proposed work is 0.781456 for the detection of cyber-attacks.

Online publication date: Tue, 30-Nov-2021

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