Phishing website detection using machine learning: A Review

Authors(2) :-Purvi Pujara, M. B. Chaudhari

Phishing is the fraudulent attempt to obtain sensitive information such as username, password, bank account details, and credit card details for malicious use. Phishing frauds might be the most popular cybercrime used today. There are various domains where phishing attack can occur like online payment sector, webmail, and financial institution, file hosting or cloud storage and many others. The webmail and online payment sector was targeted by phishing more than in any other industry sector. Several anti-phishing techniques are there such as blacklist, heuristic, visual similarity and machine learning. From this, blacklist approach is commonly used because it is easy to use and implement but it fails to detect new phishing attacks. Machine Learning is efficient technique to detect phishing. It also removes drawback of existing approach. We perform detailed literature survey and proposed new approach to detect phishing website by features extraction and machine learning algorithm.

Authors and Affiliations

Purvi Pujara
Student, Computer Department, Government Engineering College, Gandhinagar, Gujarat, India
M. B. Chaudhari
Professor, Computer Department, Government Engineering College, Gandhinagar, Gujarat, India

Phishing Detection, Feature Extraction, Phishing Website, Phishing Attacks

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Publication Details

Published in : Volume 3 | Issue 7 | September-October 2018
Date of Publication : 2018-10-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 395-399
Manuscript Number : CSEIT183783
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Purvi Pujara, M. B. Chaudhari, "Phishing website detection using machine learning: A Review", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 7, pp.395-399, September-October-2018.
Journal URL : http://ijsrcseit.com/CSEIT183783

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