Review Paper on Detection of Malicious URLs Using Machine Learning Techniques
Miss. Mayuri Pohane, A. A. Bardekar · International Journal for Research in Applied Science and Engineering Technology · 2022
Abstract: Malicious websites are most serious threats over the Web. Ever since the inception of the internet, there has been a rise in malicious content over the web such has terrorism, financial fraud, phishing and hacking that targets user’s personal information. Till today the various systems have been invent for the detection of a malicious website based on keywords and data content of the websites. This existing method 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. The proposed system is fast and more accurate compared to current system. The classifier is trained with datasets of 1000 malicious sites and 1000 legitimate site URLs. Trained classifier is used for detection of malicious URLs. Keywords: Malicious URLs, Classifier, Feature Extraction, ID3 Algorithm