Open AccessImplementation Paper on Detection of Malicious URLs Using Machine Learning Techniques
Author(s) -
Miss. Mayuri Arvind Pohane
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.41084
Subject(s) - computer science , computer security , the internet , malware , id3 , software , classifier (uml) , world wide web , data mining , artificial intelligence , operating system , decision tree , decision tree learning
Abstract: Detecting and preventing the user from the malicious site attacks are significant tasks. A huge number of attacks have been observed in last few years. Malicious attack detection and prevention system plays an immense role against these attacks by protecting the system’s critical information. The internet security software and fire walls are not enough to provide full protection to the system. Hence efficient detection systems are essential for web security. These existing methods have some drawbacks results into numbers of victims to increase. Hence we developed a system which helps the user to identify whether the website is malicious or not. Our system identifies whether the site is malicious or not through URL. Keywords: Malicious URLs, Classifier, Feature Extraction, ID3 Algorithm