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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 101–125 of 261 papers

TitleStatusHype
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning—0
Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework—0
Energy-based Models for Video Anomaly Detection—0
A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI—0
A Novel Approach To Network Intrusion Detection System Using Deep Learning For Sdn: Futuristic Approach—0
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification—0
Ensemble Classifier Design Tuned to Dataset Characteristics for Network Intrusion Detection—0
EPASAD: Ellipsoid decision boundary based Process-Aware Stealthy Attack Detector—0
Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking—0
A Transfer Learning Approach for Network Intrusion Detection—0
Flow Exporter Impact on Intelligent Intrusion Detection Systems—0
Explaining Network Intrusion Detection System Using Explainable AI Framework—0
Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection—0
Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces—0
Exploring Edge TPU for Network Intrusion Detection in IoT—0
Extreme bandits—0
Feature Analysis for Machine Learning-based IoT Intrusion Detection—0
Feature Distribution Shift Mitigation with Contrastive Pretraining for Intrusion Detection—0
Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networks—0
Feature Selection-based Intrusion Detection System Using Genetic Whale Optimization Algorithm and Sample-based Classification—0
Feature Selection for Network Intrusion Detection—0
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems—0
Decision-forest voting scheme for classification of rare classes in network intrusion detection—0
Dealing with Imbalanced Classes in Bot-IoT Dataset—0
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks—0
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