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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 26–50 of 261 papers

TitleStatusHype
PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based InferenceCode1
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural NetworkCode1
LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion DetectionCode1
Improving Transferability of Network Intrusion Detection in a Federated Learning SetupCode1
A flow-based IDS using Machine Learning in eBPFCode1
An Intrusion Detection System based on Deep Belief NetworksCode1
Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural NetworksCode1
Bridging the gap to real-world for network intrusion detection systems with data-centric approachCode1
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
A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection—0
A New Intrusion Detection System using the Improved Dendritic Cell Algorithm—0
A Network Intrusions Detection System based on a Quantum Bio Inspired Algorithm—0
Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection—0
Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier—0
Assessing Cyclostationary Malware Detection via Feature Selection and Classification—0
ASNM Datasets: A Collection of Network Traffic Features for Testing of Adversarial Classifiers and Network Intrusion Detectors—0
An AutoML-based approach for Network Intrusion Detection—0
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats—0
Active Learning for Network Intrusion Detection—0
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