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
Most implemented papers
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.
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.
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.
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.
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.
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.
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.
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.
An Intrusion Detection System based on Deep Belief Networks
The CICIDS2017 dataset was used to train and evaluate the performance of our proposed DBN approach.
Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks
This paper investigates Graph Neural Networks (GNNs) application for self-supervised network intrusion and anomaly detection.
