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Anomaly network traffic threat detection has been used in many companies to detect threats. Usually, it is deployed on-premises for efficiency and privacy reasons. Anomaly detection system usually consists of a machine learning algorithm that needs tons of time to train and test. Because each company may have different network setup, it is hard to build one system that suits for everyone. So, we need to customize the system for each client. Nevertheless, it is very inefficient to retrain the algorithm from zero for each company. Therefore, we hope to prove that by using transfer learning, we can move the knowledge from an existing detection system to a new network environment without retraining the whole algorithm.
Ali Dehghantanha
Chenxingyu Chen
eSentire Inc.
Computer science
Information and cultural industries
University of Guelph
Accelerate
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