29 citations · 80 across the 11 of their papers we have counts for
4 papers · 1 filter
SDOoop: Capturing Periodical Patterns and Out-of-phase Anomalies in Streaming Data Analysis
Alexander Hartl, Félix Iglesias Vázquez, Tanja Zseby
Streaming data analysis is increasingly required in applications, e.g., IoT, cybersecurity, robotics, mechatronics or cyber-physical systems. Despite its relevance, it is still an…
EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion Detection
Fares Meghdouri, Maximilian Bachl, Tanja Zseby
Fully Connected Neural Networks (FCNNs) have been the core of most state-of-the-art Machine Learning (ML) applications in recent years and also have been widely used for Intrusion…
SparseIDS: Learning Packet Sampling with Reinforcement Learning
Maximilian Bachl, Fares Meghdouri, Joachim Fabini +1
Recurrent Neural Networks (RNNs) have been shown to be valuable for constructing Intrusion Detection Systems (IDSs) for network data. They allow determining if a flow is malicious…
Explainability and Adversarial Robustness for RNNs
Alexander Hartl, Maximilian Bachl, Joachim Fabini +1
Recurrent Neural Networks (RNNs) yield attractive properties for constructing Intrusion Detection Systems (IDSs) for network data. With the rise of ubiquitous Machine Learning (ML)…