Detecting Phishing Website with Machine Learning
Smt.V.Priya Darshini1, P.Srilatha2, P.Neelima3
1Smt.V.Priya Darshini, Assistant Professor, SRKR Engg College, Bhimavaram, WG.Dist, AP, India.
2P.Srilatha, M.tech, SRKR Engg College, Bhimavaram, WG.Dist, AP, India.
3P.Neelima, Assistant Professor, SRKR Engg College, Bhimavaram, WG.Dist, AP, India.
Manuscript received on 14 August 2019. | Revised Manuscript received on 19 August 2019. | Manuscript published on 30 September 2019. | PP: 5626-5629 | Volume-8 Issue-3 September 2019 | Retrieval Number: K14390981119/2019©BEIESP | DOI: 10.35940/ijrte.K1439.098319
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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: Attacks are many types to disturb the network or any other websites. Phishing attacks (PA) are a type of attacks which attack the website and damage the website and may lose the data. Many types of research have been done to prevent the attacks. To overcome this, in this paper, the integrated phishing attack detection system which is adopted with SVM classifier is implemented to detect phishing websites. Phishing is the cyber attack that will destroy the website and may attack with the virus. There are two parameters that can detect the final phishing detection rate such as Identity, and security. Phishing attacks also occur in various banking and e-commerce websites. This paper deals with the UCL machine learning phishing dataset which consists of 32 attributes. The proposed algorithm implements on this dataset and shows the performance.
Keywords: Phishing Attack, Security E-Banking.
Scope of the Article: Machine Learning