Machine learning based applications for network security
An overwhelming amount of new information, technology and progress confronts us each day, supplying machine learning based applications with huge volumes of data. At the same time, new techniques for circumventing security arise and it becomes increasingly difficult to defend against each type of attack. Machine learning can be used to recognize and exploit attacks before they cause damage by optimizing detection algorithms and building an encyclopedic database of attacks. This project aims to develop the foundations for such a machine learning-based approach to security.
The research assistant will review relevant academic material concerning machine learning, cryptanalysis, cryptography and networking to develop a working knowledge of the project's core material. The assistant will investigate and implement relevant hardware and software (e.g. scikit in Python). The assistant will work with graduate students in the OPR Lab (http://www.ee.ryerson.ca/opr) and participate in regular meetings to report on research progress, difficulties, and insights. The assistant will prepare detailed, regular reports and a schedule.
Strong programming skills (CPS125, COE318, COE538), strong understanding of algorithms and digital logic (COE328, COE428). Knowledge of machine learning is an asset, but not necessary.
Reza Sedaghat : Machine learning based applications for network security | Tuesday March 21st 2017 01:19 PM