Detection of URL based phishing attacks using machine learning: A Survey

  Ms. Sophiya. Shikalgar,  Dr. S. D. Sawarkar,  Mrs. Swati Narwane

A fraud effort to get sensitive and personal information like password, username, and bank details like credit/debit card details by masking as a reliable organization in electronic communication. It most of the time redirects the users to a similar looking website as a legitimate website. The phishing website will appear the same as the legitimate website and directs the user to a page to enter personal details of the user on the fake website. The system administration is very important these days as any failure can be detected and solved instantly. The system administrator also needs to define rules and set firewall settings to avoid phishing attacks through URL. Researchers have been studying various machine learning algorithm in lines to predict and avoid phishing attacks. Through machine learning algorithms one can improve the accuracy of the prediction. The machine learning, no one algorithm works best for every problem, and it’s especially relevant for supervised learning. Using a single machine learning algorithm will give us good accuracy to predict the phishing attacks but to get better accuracy we need something more. The proposed system predicts the URL based phishing attacks with maximum accuracy. We shall talk about various machine learning, the algorithm which can help in decision making and prediction. We shall use more than one algorithm to get better accuracy of prediction. The algorithms namely the Naive Bayes and Random forest are used in the proposed system to detect URL based phishing attacks. The hybrid algorithm approach by combining two of the mentioned algorithms will increase accuracy.

Keywords- Phishing, legitimate, URL, feature extraction, machine learning, applications, classification, approach, algorithm.

Unique Identification Number - IJEDR1902111
Page Number(s) - 591-594
Pubished in - Volume 7 | Issue 2 | June 2019
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Ms. Sophiya. Shikalgar,  Dr. S. D. Sawarkar,  Mrs. Swati Narwane,   "Detection of URL based phishing attacks using machine learning: A Survey", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.7, Issue 2, pp.591-594, June 2019, Available at :http://www.ijedr.org/papers/IJEDR1902111.pdf