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

Student:

Xiaochao Luo

Partner:

eSentire Inc.

Discipline:

Computer science

Sector:

Information and cultural industries

University:

University of Guelph