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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 151–200 of 261 papers

TitleStatusHype
Toward Supervised Anomaly Detection—0
KnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs—0
Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection—0
Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection—0
Late Breaking Results: Scalable and Efficient Hyperdimensional Computing for Network Intrusion Detection—0
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT—0
LBDMIDS: LSTM Based Deep Learning Model for Intrusion Detection Systems for IoT Networks—0
Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks—0
Learning Privately from Multiparty Data—0
Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI—0
Learning to Detect: A Data-driven Approach for Network Intrusion Detection—0
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review—0
Adversarial Machine Learning in Network Intrusion Detection Systems—0
Machine Learning Applications in Misuse and Anomaly Detection—0
Machine Learning-Based Intrusion Detection: Feature Selection versus Feature Extraction—0
Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction—0
Mapping the Landscape of Generative AI in Network Monitoring and Management—0
Mimic Learning to Generate a Shareable Network Intrusion Detection Model—0
Model Selection for Anomaly Detection—0
Multi-agent Reinforcement Learning-based Network Intrusion Detection System—0
Multidomain transformer-based deep learning for early detection of network intrusion—0
Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection—0
Adversarial Examples in Constrained Domains—0
NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attacks—0
Network Intrusion Detection based on LSTM and Feature Embedding—0
Network Intrusion Detection System in a Light Bulb—0
Network Intrusion Detection Using Wrapper-based Decision Tree for Feature Selection—0
Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning—0
NIDS Neural Networks Using Sliding Time Window Data Processing with Trainable Activations and its Generalization Capability—0
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning—0
One-Shot Learning on Attributed Sequences—0
On Generalisability of Machine Learning-based Network Intrusion Detection Systems—0
Onion-Peeling Outlier Detection in 2-D data Sets—0
Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems—0
On the Evaluation of Sequential Machine Learning for Network Intrusion Detection—0
On the (Statistical) Detection of Adversarial Examples—0
PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning—0
Pelican: A Deep Residual Network for Network Intrusion Detection—0
Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection—0
POET: A Self-learning Framework for PROFINET Industrial Operations Behaviour—0
Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection—0
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats—0
PolyLUT: Ultra-low Latency Polynomial Inference with Hardware-Aware Structured Pruning—0
Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis—0
PPT-GNN: A Practical Pre-Trained Spatio-Temporal Graph Neural Network for Network Security—0
Practical Performance of a Distributed Processing Framework for Machine-Learning-based NIDS—0
A Dependable Hybrid Machine Learning Model for Network Intrusion Detection—0
Prepare for Trouble and Make it Double. Supervised and Unsupervised Stacking for AnomalyBased Intrusion Detection—0
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems—0
PWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks—0
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