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Found 9 papers, 9 papers with code
A flow-based IDS using Machine Learning in eBPF
eBPF is a new technology which allows dynamically loading pieces of code into the Linux kernel.
Detecting Fair Queuing for Better Congestion Control
Delay-based congestion control can achieve the same throughput but significantly smaller delay than loss-based one and is thus ideal for these applications.
Networking and Internet Architecture
EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion Detection
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 Detection Systems (IDSs).
LFQ: Online Learning of Per-flow Queuing Policies using Deep Reinforcement Learning
The increasing number of different, incompatible congestion control algorithms has led to an increased deployment of fair queuing.
SparseIDS: Learning Packet Sampling with Reinforcement Learning
To minimize the computational expenses of the RL-based sampling we show that a shared neural network can be used for both the classifier and the RL logic.
Explainability and Adversarial Robustness for RNNs
Recurrent Neural Networks (RNNs) yield attractive properties for constructing Intrusion Detection Systems (IDSs) for network data.
Cocoa: Congestion Control Aware Queuing
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 throughput with low queuing delay.
Networking and Internet Architecture
Walling up Backdoors in Intrusion Detection Systems
Interest in poisoning attacks and backdoors recently resurfaced for Deep Learning (DL) applications.
City-GAN: Learning architectural styles using a custom Conditional GAN architecture
Generative Adversarial Networks (GANs) are a well-known technique that is trained on samples (e. g. pictures of fruits) and which after training is able to generate realistic new samples.
