PHISH SAFE GURAD-Phishing Detection: Enhance Anti-Phishing System Using Machine Learning Algorithm
Vidya Mhaske Dhamdhere1, Sandeep Vanjale2
1Mrs. Vidya Mhaske Dhamdhere, PhD Research Scholar, Bharati Vidyapeeth Deeemed to be University, Pune (M.H), India.
2Dr. Sandeep Vanjale, Professor, Department of Computer Engg. Bharati Vidyapeeth Deeemed to be University, Pune (M.H), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1668-1671 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6839048419/19©BEIESP
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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: Today, a high usage of internet for online communication or online banking processing phishing is one of sensitive issues. Phisher can manage to gain users credentials and this causes financial loss of users. To detect phishing attack machine learning algorithms are used for classification. Anti-phishing system of PHISH SAFE-GUARD Phishing Detection (PSD-PD) based on URL features. To evaluate performance of proposed system 31 numbers of features are considered. System trained on 4601 phishing and legitimate URL with J48, Naïve Bayes, and Random forest. Our experiment shows result more than 92% accuracy in detecting phishing websites using J48 classifier.
Keywords: Phishing Websites and Emails, Phishing Detection, Classification, Machine Learning
Scope of the Article: Machine Learning