Anomalous DNS Query Detection Using Machine Learning Approaches

For organizations that use the Internet, their employees will visit thousands of websites every day. However, there is a chance that the destination website is not safe to visit. Such websites may be fraudulent, phishing, or even data-stealing related. On the other hand, determining if the target website link is suspicious or not could help to prevent potential harm. Using a filtered list is the most straightforward way. The problem is, as the database for malicious websites is growing, hackers’ minds are also developing, which requiring a more profound way to deal with such a problem. This project aims to find any anomalous website visit attempt by using machine learning algorithms to solve the problem. As eSentire is a cybersecurity company dedicated to bringing solutions to companies who are having cybersecurity concerns, this project will serve as a reference for eSentire to solve related problems with more options.

Faculty Supervisor:

Hassan Khan


Xiaochao Luo


eSentire Inc.


Computer science


Information and cultural industries


University of Guelph



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