Deep Unsupervised Learning for Network Anomaly Detection

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Network intrusion detection is a complex and evolving challenge with the increasing size and complexity of modern computernetworks and the vast quantity of data they generate. Processing this vast amount of information in today's networks is beyondhuman capabilities and necessitates automated filtering mechanisms that identify potential intrusions for human operatorinvestigation. To address this issue, Rank, Inc. will partner with the University of Toronto to develop novel anomaly detectionsystems - filtering mechanism that ingest normal activity in the network and then monitor for suspicious threats over time. Thesenovel methods are based on machine learning principles that will not only provide improved detection rate, but also supply humanoperators with explanations for identified anomalies in order to assist their decision-making process.Rank, Inc. develops solutions for monitoring network traffic to detect possible network intrusions. The proposed project seeks toimprove the toolkit developed by Rank, Inc. by advancing their anomaly detection suite with advanced concepts of deep learning.The proposed innovations are crucial for Rank, Inc. as they compete with other solution providers in the cybersecurity space andwill enable a development of a unique solution which will benefit the company from direct sales.