Comparative Analysis of Algorithms Detecting Malicious URL's.


Authors : Yashaswini J, Garima Varshney, Rupinder kaur sandhu, M Nagaraju.

Volume/Issue : Volume 3 - 2018, Issue 3 - March

Google Scholar : https://goo.gl/DF9R4u

Scribd : https://goo.gl/RNiz7b

Thomson Reuters ResearcherID : https://goo.gl/3bkzwv

With the advent of technology, any piece of information can be retrieved from internet by giving an appropriate URL. But this can also be the source of criminal activities causing the URL to be malicious. Such websites contain unwanted contents like phishing sites, spam-advertised products, dangerous “drive-by” harness that infect a visitor’s system with malware. Using URL features such as length of URL, domain of URL, Presence of Ip Address in Host Name, Presence of Security Sensitive Words in URL and many more which are relying on the fact that users directly deal with URLs to surf the internet and provides a good approach to detect malicious URLs. In this paper we perform comparative analysis of various machine learning algorithms such as logistic regression, decision trees, gradient boosting, Random forest and adaboosting on various performance metrics like their learning approach, accuracy and many more in detecting malicious content of URL’s.

Keywords : logistic regression, Decision tress, Adaboosting, Gradient boosting, Random forest.

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