Monitoring traffic in computer networks is one of the core approaches for defending critical infrastructure against cyber attacks.
Centuries of development in natural sciences and mathematical modeling provide valuable domain expert knowledge that has yet to be explored for the development of machine learning models.
Adversarial machine learning offers an approach to increase our understanding of these models.
Experimental results showed that the standard LSTM failed at one-minute resolution data while performing well in one-hour resolution data.
While most current trajectory simplification algorithms are tailored for GPS trajectories, our approach focuses on smooth trajectories for robot programming by demonstration recorded using motion capture systems. Two variations of the algorithm are presented: 1. aims to preserve shape and temporal information; 2. preserves only shape information.