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Network Intrusion Detection

Network intrusion detection is the task of monitoring network traffic to and from all devices on a network in order to detect computer attacks.

Papers

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Showing 1–25 of 261 papers

TitleStatusHype
CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for Network Intrusion DetectionCode1
Continual Learning with Strategic Selection and Forgetting for Network Intrusion DetectionCode1
FedMSE: Federated learning for IoT network intrusion detectionCode1
XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language ModelCode1
PolyLUT-Add: FPGA-based LUT Inference with Wide InputsCode1
Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural NetworksCode1
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural NetworkCode1
On the Cross-Dataset Generalization of Machine Learning for Network Intrusion DetectionCode1
Improving Transferability of Network Intrusion Detection in a Federated Learning SetupCode1
A Study on Transferability of Deep Learning Models for Network Intrusion DetectionCode1
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly GenerationCode1
LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion DetectionCode1
netFound: Foundation Model for Network SecurityCode1
IoTGeM: Generalizable Models for Behaviour-Based IoT Attack DetectionCode1
PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based InferenceCode1
SoK: Pragmatic Assessment of Machine Learning for Network Intrusion DetectionCode1
FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection SystemsCode1
TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial NetworksCode1
Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural NetworksCode1
An Intrusion Detection System based on Deep Belief NetworksCode1
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly DetectionCode1
Representation Learning for Content-Sensitive Anomaly Detection in Industrial NetworksCode1
Bridging the gap to real-world for network intrusion detection systems with data-centric approachCode1
Unveiling the potential of Graph Neural Networks for robust Intrusion DetectionCode1
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
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