Authors: Parvinder Singh; Bhawna Sharma; Jasvinder Kaur
Addresses: Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat, Haryana, India ' Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat, Haryana, India ' Computer Science and Engineering, PDM University, Bahadurgarh, Haryana, India
Abstract: Phishing has represented a more noteworthy danger to clients. In the current work, the authors attempted to build up a powerful anti-phishing technique based on hybrid similarity approach combining Cosine and Soft Cosine similarity that measures the resemblance between user query and database. The proposed similarity hybrid is also evaluated against another similarity hybrid comprising of Cosine and Jaccard similarity measure so as to validate the proposed work. Both hybrid similarities are separately fed to validation layer of feed forward back propagation neural network (FFBPNN) to predict phishing and legitimate websites. The performance of the proposed work is evaluated against data set comprising of 3,000 sample files in terms of positive predictive value (PPV), true positive rate (TPR) and F-measure. The comparative analysis demonstrated that the anti-phishing model using proposed similarity hybrid outperformed the cosine and Jaccard similarity hybrid with 0.233%, 0.2833% and 0.258% higher PPV, TPR and F-measure, respectively.
Keywords: phishing; cosine similarity; soft-cosine similarity; similarity index; FFBPNN.
International Journal of Computational Vision and Robotics, 2022 Vol.12 No.2, pp.141 - 155
Received: 19 Oct 2020
Accepted: 08 Dec 2020
Published online: 24 Jan 2022 *