29 citations · 80 across the 11 of their papers we have counts for
11 papers
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…
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…
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…
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…
Walling up Backdoors in Intrusion Detection Systems
Maximilian Bachl, Alexander Hartl, Joachim Fabini +1
Interest in poisoning attacks and backdoors recently resurfaced for Deep Learning (DL) applications. Several successful defense mechanisms have been recently proposed for Convoluti…
Encryption is Futile: Delay Attacks on High-Precision Clock Synchronization
Robert Annessi, Joachim Fabini, Felix Iglesias +1
Clock synchronization has become essential to modern societies since many critical infrastructures depend on a precise notion of time. This paper analyzes security aspects of high-…
SecureTime: Secure Multicast Time Synchronization
Robert Annessi, Joachim Fabini, Tanja Zseby
Due to the increasing dependency of critical infrastructure on synchronized clocks, network time synchronization protocols have become an attractive target for attackers. We identi…