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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 101–110 of 261 papers

TitleStatusHype
Towards Reliable Rare Category Analysis on Graphs via Individual CalibrationCode0
Machine Learning-Based Intrusion Detection: Feature Selection versus Feature Extraction—0
Host-Based Network Intrusion Detection via Feature Flattening and Two-stage Collaborative Classifier—0
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning—0
Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation—0
SoK: Pragmatic Assessment of Machine Learning for Network Intrusion DetectionCode1
POET: A Self-learning Framework for PROFINET Industrial Operations Behaviour—0
FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection SystemsCode1
Late Breaking Results: Scalable and Efficient Hyperdimensional Computing for Network Intrusion Detection—0
BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion Detection—0
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