29 citations · 80 across the 9 of their papers we have counts for
9 papers
A flow-based IDS using Machine Learning in eBPF
Maximilian Bachl, Joachim Fabini, Tanja Zseby
eBPF is a new technology which allows dynamically loading pieces of code into the Linux kernel. It can greatly speed up networking since it enables the kernel to process certain pa…
Detecting Fair Queuing for Better Congestion Control
Maximilian Bachl, Joachim Fabini, Tanja Zseby
Low delay is an explicit requirement for applications such as cloud gaming and video conferencing. Delay-based congestion control can achieve the same throughput but significantly…
LFQ: Online Learning of Per-flow Queuing Policies using Deep Reinforcement Learning
Maximilian Bachl, Joachim Fabini, Tanja Zseby
The increasing number of different, incompatible congestion control algorithms has led to an increased deployment of fair queuing. Fair queuing isolates each network flow and can t…
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)…
Cocoa: Congestion Control Aware Queuing
Maximilian Bachl, Joachim Fabini, Tanja Zseby
Recent model-based congestion control algorithms such as BBR use repeated measurements at the endpoint to build a model of the network connection and use it to achieve optimal thro…