Real-time DNS tunneling detection using Machine Learning & Deep Learning techniques

Domain Name System (DNS) tunneling is a malicious technique that enables attackers to bypass network security
measures and steal sensitive information. Traditional detection methods that rely on signature-based approaches
are often ineffective against advanced attacks. In light of this, recent years have seen a growing interest in the
use of deep learning techniques for network intrusion detection. This research aims to explore the feasibility of
using deep learning algorithms for the detection of DNS tunneling in real-time network traffic. The study will involve
the analysis of a large dataset of DNS traffic to develop and evaluate a deep learning-based model. The model’s
performance will be compared against existing detection methods, and the outcomes of this research will
contribute to improving the effectiveness of DNS tunneling detection, enhancing network security overall.

Faculty Supervisor:

Murat Erdogdu

Student:

Partner:

BlueCat Networks

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program: