240 citations · 241 across the 4 of their papers we have counts for
7 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…
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…
Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item Prediction
Elisabeth Fischer, Albin Zehe, Andreas Hotho +1
Analyzing sequences of interactions between users and items, sequential recommendation models can learn user intent and make predictions about the next item. Next to item interacti…
ModeConv: A Novel Convolution for Distinguishing Anomalous and Normal Structural Behavior
Melanie Schaller, Daniel Schlör, Andreas Hotho
External influences such as traffic and environmental factors induce vibrations in structures, leading to material degradation over time. These vibrations result in cracks due to t…
Towards Responsible Medical Diagnostics Recommendation Systems
Daniel Schlör, Andreas Hotho
The early development and deployment of hospital and healthcare information systems have encouraged the ongoing digitization of processes in hospitals. Many of these processes, whi…
iNALU: Improved Neural Arithmetic Logic Unit
Daniel Schlör, Markus Ring, Andreas Hotho
Neural networks have to capture mathematical relationships in order to learn various tasks. They approximate these relations implicitly and therefore often do not generalize well.…
Flow-based Network Traffic Generation using Generative Adversarial Networks
Markus Ring, Daniel Schlör, Dieter Landes +1
Flow-based data sets are necessary for evaluating network-based intrusion detection systems (NIDS). In this work, we propose a novel methodology for generating realistic flow-based…