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Paper Title : MALICIOUS URL DETECTION USING MACHINE LEARNING

ISSN : 2395-1303
Year of Publication : 2022
10.5281/zenodo.7221385

Authors: Mr.Ayub Baig, Rangineni Anjali, Vakkalagadda Kavya, Vemishetti Uma rani

         


Abstract
The simplest approach to get sensitive information from unwitting people is through a phishing attack. The goal of phishers is to obtain crucial data, such as username, password, and bank account information. People working in cyber security are currently looking for reliable and consistent methods of detecting phishing websites. In order to distinguish between legal and phishing URLs, this article uses machine learning technology. It extracts and analyses many aspects of both types of URLs. Algorithms such as Support Vector Machine, Decision Tree, and Random Forest are used to identify phishing websites. The paper's objective is to identify phishing URLs and identify the best machine learning method by evaluating each algorithm's accuracy rate, false positive rate, and false negative rate.

Reference
[1]Gunter Ollmann, “The Phishing Guide Understanding & Preventing Phishing Attacks”, IBM Internet Security Systems, 2007. [2]https://resources.infosecinstitute.com/category/enterprise /phishing/the- phishing-landscape/phishing-data-attack-statistics/#gref [3]Mahmoud Khonji, Youssef Iraqi, "Phishing Detection: A Literature Survey IEEE, and Andrew Jones, 2013 [4]Mohammad R., Thabtah F. McCluskey L., (2015) Phishing websites dataset. Available: https://archive.ics.uci.edu/ml/datasets/Phishing+Websites Accessed January 2016 [5]http://dataaspirant.com/2017/01/30/how-decision-tree-algorithm-works/ http://dataaspirant.com/2017/05/22/random-forest-algorithm-machine- learing/ [6]https://www.kdnuggets.com/2016/07/support-vector-machines-simple- explanation.html [7]www.alexa.com [8]www.phishtank.com
Keywords
— MALICIOUS URL DETECTION USING MACHINE LEARNING