An analysis of the challenges of building machine learning-based intrusion detection systems

Network attacks are becoming more complex every day. It is crucial that we use tools that can detect these sophisticated attacks on networks so that we can identify malicious behavior and prevent attacks and intrusions. The use of machine learning to create intrusion detection engines is great, and we need enough data to train these engines. The purpose of this project is to analyze the problems of existing public datasets and the challenges involved in finding the right machine learning techniques and settings for them.

Faculty Supervisor:

Jonathan Anderson

Student:

Partner:

NASDAQ Canada Inc

Discipline:

Computer science

Sector:

Finance and Insurance; Information and Communications Technology; Technology

University:

Memorial University of Newfoundland

Program:

Accelerate

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