In the networking field, network topology is one of the most important perspectives as it can bring additional insights to the modeling process. Existing anomaly detection approaches do not take topology information into consideration. To address such limitations techniques are presented herein that support deep Convolutional Neural Network (CNN) modeling with Reinforcement Learning (RL) employing, for example, an Advantage Actor Critic (A2C) algorithm. Additionally, aspects of the techniques presented herein support an innovative new way to model a "customer profile" that leverages topology information.
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Shao, Qihong and Pignataro, Carlos M., "SELF-ADAPTIVE ANOMALY DETECTION WITH DEEP REINFORCEMENT LEARNING AND TOPOLOGY", Technical Disclosure Commons, (January 06, 2021)