PhishAlert: An Efficient Phishing URL Detection via Hybrid Methodology
Bhawna Sharma1, Parvinder Singhi2
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.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 3936-3941 | Volume-8 Issue-12, October 2019. | Retrieval Number: L34551081219/2019©BEIESP | DOI: 10.35940/ijitee.L3455.1081219
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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: In spite of various research endeavors, phishing assaults stay common and exceedingly successful in attracting clueless clients to uncover delicate data, including account details and government managed savings numbers. Misfortunes due to phishing are developing consistently. A solitary methodology isn’t effective for distinguishing a wide range of phishing assaults. So we propose a hybrid approach to deal with the classification of URLs as phishing or real. The investigation aftereffects of our proposed methodology, in view of a dataset gathered from phishing and legitimate URLs, have demonstrated that PhishAlert framework can successfully counteract phishing assaults and can thus ensure system security.
Keywords: Phishing, Whitelist, Heuristics, Style Similarity, Hybrid Approach
Scope of the Article: Classification