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Machine Learning Classifiers to Detect Malicious Websites

A risk that exists in Internet is the access of websites with malicious content, because they might be open doors for cybercrimes or be the mechanism to download files in order to affect organizations, persons and the environment. What is more, the attack registers through websites have been part of cyberattacks reports during the last years; this information includes attacks made by the currently risks found in new technologies, such as the IoT. Due the computer security complexity, studies have been working in to use machine learning algorithms to identify web malicious content. This article explores the application of a data analysis process through a framework that includes dynamic, static analysis, updated websites and a low interaction client honeypot in order to classify a website. Furthermore, it evaluates the capacity of the classification of four machine learning through the information analyzed.