Reference number : GC-2017-Q2-00429, amendment number : , agreement type : G, recipient legal name : Sanner, Scott (University of Toronto), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Network intrusion detection is a complex and evolving challenge with the increasing size and complexity of modern computer_x000D_
networks and the vast quantity of data they generate. Processing this vast amount of information in today's networks is beyond_x000D_
human capabilities and necessitates automated filtering mechanisms that identify potential intrusions for human operator_x000D_
investigation. To address this issue, Rank, Inc. will partner with the University of Toronto to develop novel anomaly detection_x000D_
systems - filtering mechanism that ingest normal activity in the network and then monitor for suspicious threats over time. These_x000D_
novel methods are based on machine learning principles that will not only provide improved detection rate, but also supply human_x000D_
operators with explanations for identified anomalies in order to assist their decision-making process._x000D_
Rank, Inc. develops solutions for monitoring network traffic to detect possible network intrusions. The proposed project seeks to_x000D_
improve the toolkit developed by Rank, Inc. by advancing their anomaly detection suite with advanced concepts of deep learning._x000D_
The proposed innovations are crucial for Rank, Inc. as they compete with other solution providers in the cybersecurity space and_x000D_
will enable a development of a unique solution which will benefit the company from direct sales., program purpose french : Network intrusion detection is a complex and evolving challenge with the increasing size and complexity of modern computer_x000D_
networks and the vast quantity of data they generate. Processing this vast amount of information in today's networks is beyond_x000D_
human capabilities and necessitates automated filtering mechanisms that identify potential intrusions for human operator_x000D_
investigation. To address this issue, Rank, Inc. will partner with the University of Toronto to develop novel anomaly detection_x000D_
systems - filtering mechanism that ingest normal activity in the network and then monitor for suspicious threats over time. These_x000D_
novel methods are based on machine learning principles that will not only provide improved detection rate, but also supply human_x000D_
operators with explanations for identified anomalies in order to assist their decision-making process._x000D_
Rank, Inc. develops solutions for monitoring network traffic to detect possible network intrusions. The proposed project seeks to_x000D_
improve the toolkit developed by Rank, Inc. by advancing their anomaly detection suite with advanced concepts of deep learning._x000D_
The proposed innovations are crucial for Rank, Inc. as they compete with other solution providers in the cybersecurity space and_x000D_
will enable a development of a unique solution which will benefit the company from direct sales., agreement title english : Deep Unsupervised Learning for Network Anomaly Detection, agreement title french : Deep Unsupervised Learning for Network Anomaly Detection, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-08-23, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada