39 citations · 41 across the 10 of their papers we have counts for
5 papers · 1 filter
Benchmarking Sensor-Fault Robustness in Forecasting
Alexander Windmann, Philipp Wittenberg, Gianluca Manca +3
Cyber-physical system (CPS) forecasting models depend on sensor streams with noisy, biased, missing, or temporally misaligned readings, yet standard forecasting evaluation often se…
MAWIFlow Benchmark: Realistic Flow-Based Evaluation for Network Intrusion Detection
Joshua Schraven, Alexander Windmann, Oliver Niggemann
Benchmark datasets for network intrusion detection commonly rely on synthetically generated traffic, which fails to reflect the statistical variability and temporal drift encounter…
Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical Systems
Alexander Windmann, Henrik Steude, Daniel Boschmann +1
Cyber-Physical Systems (CPS) in domains such as manufacturing and energy distribution generate complex time series data crucial for Prognostics and Health Management (PHM). While D…
Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study
Alexander Windmann, Henrik Steude, Oliver Niggemann
Deep learning (DL) models have seen increased attention for time series forecasting, yet the application on cyber-physical systems (CPS) is hindered by the lacking robustness of th…
Learning Physical Concepts in Cyber-Physical Systems: A Case Study
Henrik S. Steude, Alexander Windmann, Oliver Niggemann
Machine Learning (ML) has achieved great successes in recent decades, both in research and in practice. In Cyber-Physical Systems (CPS), ML can for example be used to optimize syst…