A Review on Malicious URL Detection using Machine Learning Systems

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Dipali K. Karnase, Megha G. Mishra, Snehal H. Dighole, Snehal R. Shelke, Mr. D. C. Dhanwani

Abstract

Malicious web sites pretendsignificant danger to desktop security and privacy.These links become instrumental in giving partial or full system control to the attackers. This results in victim systems, which get easily infected and, attackers can utilize systems for various cyber-crimes such as stealing credentials, spamming, phishing, denial-of-service and many more such attack. Detection of such website is difficult because of thephishing campaigns and the efforts to avoid blacklists.To look for malicious URLs, the first step is usually to gather URLs that are liveon the Internet. There are various stages to detect this URLs such as collection of dataset, extracting feature using different feature extraction techniques and Classification of extracted feature. This paper focus on comparative analysis of malicious URL detection techniques.

Article Details

How to Cite
, D. K. K. M. G. M. S. H. D. S. R. S. M. D. C. D. “A Review on Malicious URL Detection Using Machine Learning Systems”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 6, no. 4, Apr. 2018, pp. 214-9, doi:10.17762/ijritcc.v6i4.1547.
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