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Machine Learning for Web Proxy Analytics

Machine Learning for Web Proxy Analytics

Mark Maldonado, Ayad Barsoum
Copyright: © 2019 |Volume: 1 |Issue: 2 |Pages: 12
ISSN: 2577-4816|EISSN: 2577-4824|EISBN13: 9781522563587|DOI: 10.4018/IJCRE.2019070103
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MLA

Maldonado, Mark, and Ayad Barsoum. "Machine Learning for Web Proxy Analytics." IJCRE vol.1, no.2 2019: pp.30-41. http://doi.org/10.4018/IJCRE.2019070103

APA

Maldonado, M. & Barsoum, A. (2019). Machine Learning for Web Proxy Analytics. International Journal of Cyber Research and Education (IJCRE), 1(2), 30-41. http://doi.org/10.4018/IJCRE.2019070103

Chicago

Maldonado, Mark, and Ayad Barsoum. "Machine Learning for Web Proxy Analytics," International Journal of Cyber Research and Education (IJCRE) 1, no.2: 30-41. http://doi.org/10.4018/IJCRE.2019070103

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Abstract

Proxy servers used around the globe are typically graded and built for small businesses to large enterprises. This does not dismiss any of the current efforts to keep the general consumer of an electronic device safe from malicious websites or denying youth of obscene content. With the emergence of machine learning, we can utilize the power to have smart security instantiated around the population's everyday life. In this work, we present a simple solution of providing a web proxy to each user of mobile devices or any networked computer powered by a neural network. The idea is to have a proxy server to handle the functionality to allow safe websites to be rendered per request. When a website request is made and not identified in the pre-determined website database, the proxy server will utilize a trained neural network to determine whether or not to render that website. The neural network will be trained on a vast collection of sampled websites by category. The neural network needs to be trained constantly to improve decision making as new websites are visited.

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