Anomaly detection on edge networks
Network anomaly detection is an active research area, with numerous solutions that utilize statistical methods, neural networks, and other machine learning methods. Autoencoders, in particular, have shown great performance by learning representations solely from benign traffic, enabling the detection of zero-day attacks. In this thesis, we propose a methodology that uses transfer learning to train