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MSc Thesis Proposal Announcement by William Briguglio:"Machine Learning Interpretability in Cyber Threat Detection "

Thursday, November 21, 2019 - 10:00 to 12:00


The School of Computer Science at the University of Windsor is pleased to present …


MSc Thesis Proposal by William Briguglio

Date:  Thursday, Nov. 21st, 2019
Time:  10:00am – 12:00pm
Location: 3105 Lambton Tower


In cyberattack detection and prevention systems, cybersecurity analysts always prefer solutions that are as interpretable and understandable 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 cyber threat detection. This is because the models are complex, and most of them work as a black-box. Further, the increased ability for malware authors to bypass antimalware systems has forced security specialists to look to machine learning for creating robust detection systems. If these systems are to be relied on in the industry, then, among other challenges, they must also explain their predictions.  The objective of this research is to evaluate the current state-of-the-art ML models interpretability techniques when applied to ML-based cyber threat detectors. We demonstrate interpretability techniques in practice and evaluate the effectiveness of existing interpretability techniques in the cyber threat detection domain.

Thesis Committee:

Internal Reader: Dr. Alioune Ngom
External Reader: Dr. Mitra Mirhassani
Advisor: Dr. Sherif Saad

MSc Thesis Proposal Announcement


5113 Lambton Tower, 401 Sunset Ave., Windsor ON, N9B 3P4 (519) 253-3000 Ext. 3716