5 papers
Evaluating Tabular Representation Learning for Network Intrusion Detection
Muhammad Usman Butt, Andreas Hotho, Daniel Schlör
Classic Network Intrusion Detection Systems (NIDS) often rely on manual feature engineering to extract meaningful patterns from network traffic data. However, this approach require…
Exploring Design Choices for Autoregressive Deep Learning Climate Models
Florian Gallusser, Simon Hentschel, Anna Krause +1
Deep Learning models have achieved state-of-the-art performance in medium-range weather prediction but often fail to maintain physically consistent rollouts beyond 14 days. In cont…
Physical knowledge improves prediction of EM Fields
Andrzej Dulny, Farzad Jabbarigargari, Andreas Hotho +3
We propose a 3D U-Net model to predict the spatial distribution of electromagnetic fields inside a radio-frequency (RF) coil with a subject present, using the phase, amplitude, and…
Systematic Evaluation of Synthetic Data Augmentation for Multi-class NetFlow Traffic
Maximilian Wolf, Dieter Landes, Andreas Hotho +1
The detection of cyber-attacks in computer networks is a crucial and ongoing research challenge. Machine learning-based attack classification offers a promising solution, as these…
GrINd: Grid Interpolation Network for Scattered Observations
Andrzej Dulny, Paul Heinisch, Andreas Hotho +1
Predicting the evolution of spatiotemporal physical systems from sparse and scattered observational data poses a significant challenge in various scientific domains. Traditional me…