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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 176–200 of 261 papers

TitleStatusHype
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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