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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 31–40 of 261 papers

TitleStatusHype
An Intrusion Detection System based on Deep Belief NetworksCode1
Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural NetworksCode1
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural NetworkCode1
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networksCode1
An Interpretable Federated Learning-based Network Intrusion Detection Framework—0
An incremental hybrid adaptive network-based IDS in Software Defined Networks to detect stealth attacks—0
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning—0
Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection—0
A new semi-supervised inductive transfer learning framework: Co-Transfer—0
Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems—0
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