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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 51–75 of 261 papers

TitleStatusHype
Diffusion-based Adversarial Purification for Intrusion DetectionCode0
PPT-GNN: A Practical Pre-Trained Spatio-Temporal Graph Neural Network for Network Security—0
Detection-Rate-Emphasized Multi-objective Evolutionary Feature Selection for Network Intrusion Detection—0
Sequential Binary Classification for Intrusion Detection—0
PolyLUT-Add: FPGA-based LUT Inference with Wide InputsCode1
Generative AI-in-the-loop: Integrating LLMs and GPTs into the Next Generation Networks—0
Strengthening Network Intrusion Detection in IoT Environments with Self-Supervised Learning and Few Shot Learning—0
A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI—0
KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation—0
Practical Performance of a Distributed Processing Framework for Machine-Learning-based NIDS—0
Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection—0
Large Language Models for Cyber Security: A Systematic Literature ReviewCode0
Feature Distribution Shift Mitigation with Contrastive Pretraining for Intrusion Detection—0
Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection—0
An incremental hybrid adaptive network-based IDS in Software Defined Networks to detect stealth attacks—0
Dealing with Imbalanced Classes in Bot-IoT Dataset—0
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning—0
Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural NetworksCode1
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats—0
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural NetworkCode1
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection—0
On the Cross-Dataset Generalization of Machine Learning for Network Intrusion DetectionCode1
Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction—0
Deep Learning Applications for Intrusion Detection in Network TrafficCode0
Improving Transferability of Network Intrusion Detection in a Federated Learning SetupCode1
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