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Anomaly detection from system logs through deep learning
During the last decade, we observe in organizations a surge of numbers of cyber-attacks originating internally. In this project, we aim to develop deep learning models to detect suspicious activity (such as malicious events, system failure or attacks) from log data generated by the Desjardins ecosystem.
Faculty Supervisor:
Marc Frappier;Pierre-Martin Tardif;Froduald Kabanza
Student:
Partner:
Mouvement des caisses Desjardins
Discipline:
Computer science
Sector:
Finance and Insurance
University:
Université de Sherbrooke
Program:
Accelerate
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