Antiphishing Model Based on Similarity Index and Neural Networks
Bhawna Sharma1, Parvinder Singh2, Jasvinder Kaur3, Pablo García Bringas4
1Bhawna Sharma*, Research Scholar, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Sonepat, Haryana, India.
2Dr. Parvinder Singh, Professor, Department of Computer Science & Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Sonepat, Haryana, India.
3Dr. Jasvinder Kaur, Assistant Professor, Department of Computer Science & Engineering, PDM University, Bahadurgarh, Haryana, India.
4Dr. Pablo García Bringas, Professor, University of Deusto. Spain.
Manuscript received on September 15, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 4114-4119 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1350109119/2019©BEIESP | DOI: 10.35940/ijeat.A1350.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Phishing is a negative technique that is used to steel private and confidential information over the web. In the present work author proposed a hybrid similarity of Cosine and Soft Cosine to calculate the similarity between the user query and repository as an anti-phishing approach. The proposed work model uses a multiclass learning method called Feed Forward Back Propagation Neural Network. The model evaluation results with 100 to 3000 test files shows that the hybrid model is able to detect the phishing attack with an average precision of 71% and is highly effective.
Keywords: Phishing, Similarity, Neural Network.