A three-year fully funded Ph.D. studentship is available from September 1st, 2019 at University of Windsor, Ontario, Canada (http://www.uwindsor.ca/)
Cybersecurity analysts always prefer solutions that are interpretable and understandable, such as rule-based or signature-based detection. This is because of the need to tune and optimize these solutions to mitigate and control the effect of false positives and false negatives. Interpreting machine learning models is a new and open challenge. However, it is expected that an interpretable machine learning solution will be domain-specific, for instance, interpretable solutions for machine learning models in healthcare are different than solutions in malware detection.
The research project studies the limitations of applying machine learning in cybersecurity (e.g., malware detection, opinion spam, credit frauds, etc.).
In particular interpretation of machine learning models in cybersecurity application. The project’s main goal is to establish and implement a model-agnostic technique that will enable independent review and validation of machine learning models in the cybersecurity domain.
The studentship is open to all applicants although applicants from outside Canada will be required to cover the annual difference between Canada and the overseas tuition fee rates.
- BSc and MSc Degrees in Computer Science
- Candidates should have an excellent academic track record. With GPA not less than 3.5 out of 4.0 for BSc and MSc.
- Knowledge of Cybersecurity key concepts and best practice is a plus.
- Programming skills: R or Python (and related data science libraries)
- Machine Learning and Artificial Intelligence techniques: classification, regression, clustering, neural networks, support vector machines, decisions trees, dimensionality reduction, performance estimation
- Mathematical and statistical modeling: regression, general linear models, stochastic processes, time series analysis
- Experience with handling large datasets (SQL, CSV)
- Knowledge of Cybersecurity is a plus
Nice To Know:
- Python | Scikit-Learn | Pandas & Numpy | Tensorflow | H2O AutoML | Alteryx | Tableau
Interested applicants should send a cover letter, resume, scanned copies of their BSc and MSc transcripts, MSc thesis abstract, at least one sample publication, and clearly describe any first-hand experience with machine learning.
The required documents should be sent to Dr. Saad (firstname.lastname@example.org) by email with subject-line “SGWASP_012019”