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Detection of phishing website using machine learning/URL

 Department: Computer Science  
 By: usericon SIRJOSEPH  

 Project ID: 7220
   Rating:  (3.7) votes: 3
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We are exposed to great risks as we moved most of our financial, work related to and other daily activities to the internet. URL based phishing attacks are one of the most common threats to the internet users. In this type of attack, the attacker target exploits the human vulnerability rather than software flows. It target both individuals and organizations, induces them to click on URLs that look secure, and steal confidential information or inject malware in a system. Different machine learning algorithms are being used for the detection of phishing URLs that is to classify a URL as phishing or legitimate. The goal is to develop a system that can predict whether URL is phishing or legitimate. The data-set used in this study was sourced from phish Tank and open Phish in the form of Json and csv and also from Mendeley Data website where the data set sample is downloaded. The data set consist of collections of legitimate as well as phishing website instances. The data set serves as an input for the machine learning process. This study aims to develop a system that detects legitimate and phishing URLs from the instances collected for legitimate and phishing websites. The total number of legitimate website instances is 50,000 while the number of phishing website instances is 30,000 in the data-set sample. Address Bar based feature, Abnormal based feature, HTML and JavaScript based features, Domain based features. The above mentioned features are used in designing and analyzing machine learning models in order to form data-set that can be used for training and testing. HTML, JavaScript, PHP and Mysql are the technologies used in the development process of the phishing detection system. In conclusion phishing detection system is now a great interest for organization...
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