Network Intrusion Detection
47 papers with code • 5 benchmarks • 12 datasets
Network intrusion detection is the task of monitoring network traffic to and from all devices on a network in order to detect computer attacks.
Benchmarks
These leaderboards are used to track progress in Network Intrusion Detection
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Libraries
Use these libraries to find Network Intrusion Detection models and implementationsDatasets
UNSW-NB15
CICIDS2017
SIDD-Image
CIC IoT Dataset 2022
IoT Network Intrusion Dataset
IoT Benign and Attack Traces
IoT ENVIRONMENT DATASET
Latest papers
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection
bit-ml/anoshift
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Analyzing the distribution shift of data is a growing research direction in nowadays Machine Learning (ML), leading to emerging new benchmarks that focus on providing a suitable scenario for studying the generalization properties of ML models.
Representation Learning for Content-Sensitive Anomaly Detection in Industrial Networks
Using a convGRU-based autoencoder, this thesis proposes a framework to learn spatial-temporal aspects of raw network traffic in an unsupervised and protocol-agnostic manner.
The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems
By using XeNIDS on six well-known datasets, we demonstrate the concealed potential, but also the risks, of cross-evaluations of ML-NIDS.
Bridging the gap to real-world for network intrusion detection systems with data-centric approach
Most research using machine learning (ML) for network intrusion detection systems (NIDS) uses well-established datasets such as KDD-CUP99, NSL-KDD, UNSW-NB15, and CICIDS-2017.
Unveiling the potential of Graph Neural Networks for robust Intrusion Detection
BNN-UPC/GNN-NIDS
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To this end, we use a graph representation that keeps flow records and their relationships, and propose a novel Graph Neural Network (GNN) model tailored to process and learn from such graph-structured information.
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs).
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.
Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural Network
racsa-lab/EDD
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Our results demonstrate that in comparison to conventional DLM techniques, our model maintains a high testing accuracy of 99% even with lower resource utilization in terms of cpu and memory.
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated Learning
yuweisunn/segmented-FL
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IEEE Open Journal of the Communications Society (Conference version: IJCNN) 2020
We propose Segmented-Federated Learning (Segmented-FL), where by employing periodic local model evaluation and network segmentation, we aim to bring similar network environments to the same group.
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANs
yuweisunn/segmented-FL
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International Joint Conference on Neural Networks (IJCNN) 2020
In this research, a segmented federated learning is proposed, different from a collaborative learning based on single global model in a traditional federated learning model, it keeps multiple global models which allow each segment of participants to conduct collaborative learning separately and rearranges the segmentation of participants dynamically as well.
