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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 11–20 of 261 papers

TitleStatusHype
Continual Learning with Strategic Selection and Forgetting for Network Intrusion DetectionCode1
A Study on Transferability of Deep Learning Models for Network Intrusion DetectionCode1
CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for Network Intrusion DetectionCode1
Bridging the gap to real-world for network intrusion detection systems with data-centric approachCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
An Intrusion Detection System based on Deep Belief NetworksCode1
Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural NetworksCode1
A flow-based IDS using Machine Learning in eBPFCode1
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly DetectionCode1
IoTGeM: Generalizable Models for Behaviour-Based IoT Attack DetectionCode1
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