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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 1–10 of 261 papers

TitleStatusHype
Bridging the gap to real-world for network intrusion detection systems with data-centric approachCode1
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly DetectionCode1
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural NetworkCode1
A Study on Transferability of Deep Learning Models for Network Intrusion DetectionCode1
Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural NetworksCode1
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networksCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
An Intrusion Detection System based on Deep Belief NetworksCode1
A flow-based IDS using Machine Learning in eBPFCode1
CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for Network Intrusion DetectionCode1
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