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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
Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection—0
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks—0
Early Detection of Network Attacks Using Deep Learning—0
One-Shot Learning on Attributed Sequences—0
An Interpretable Federated Learning-based Network Intrusion Detection Framework—0
Feature Selection-based Intrusion Detection System Using Genetic Whale Optimization Algorithm and Sample-based Classification—0
Detect & Reject for Transferability of Black-box Adversarial Attacks Against Network Intrusion Detection Systems—0
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review—0
Two-stage Deep Stacked Autoencoder with Shallow Learning for Network Intrusion Detection System—0
Improving the Reliability of Network Intrusion Detection Systems through Dataset Integration—0
Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion DetectionCode0
A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection—0
Bridging the gap to real-world for network intrusion detection systems with data-centric approachCode1
PWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks—0
From Zero-Shot Machine Learning to Zero-Day Attack Detection—0
Feature Analysis for Machine Learning-based IoT Intrusion Detection—0
Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networks—0
Learning to Detect: A Data-driven Approach for Network Intrusion Detection—0
A new semi-supervised inductive transfer learning framework: Co-Transfer—0
Unveiling the potential of Graph Neural Networks for robust Intrusion DetectionCode1
Decision-forest voting scheme for classification of rare classes in network intrusion detection—0
Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks—0
Segmented Federated Learning for Adaptive Intrusion Detection System—0
On the Evaluation of Sequential Machine Learning for Network Intrusion Detection—0
Towards a Privacy-preserving Deep Learning-based Network Intrusion Detection in Data Distribution Services—0
Cybersecurity Anomaly Detection in Adversarial Environments—0
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT—0
Benchmarking the Benchmark -- Analysis of Synthetic NIDS Datasets—0
Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection—0
Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review—0
Exploring Edge TPU for Network Intrusion Detection in IoT—0
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
Explaining Network Intrusion Detection System Using Explainable AI Framework—0
TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack—0
A flow-based IDS using Machine Learning in eBPFCode1
Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural NetworkCode1
Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacy—0
Intrusion detection in IoT using artificial neural networks on UNSW-15 dataset—0
Towards Network Traffic Monitoring Using Deep Transfer Learning—0
A Novel Resampling Technique for Imbalanced Dataset Optimization—0
A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset—0
RNNIDS: Enhancing Network Intrusion Detection Systems through Deep Learning—0
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
Review: Deep Learning Methods for Cybersecurity and Intrusion Detection Systems—0
Adversarial Examples in Constrained Domains—0
DualNet: Locate Then Detect Effective Payload with Deep Attention Network—0
Spiking Neural Networks with Single-Spike Temporal-Coded Neurons for Network Intrusion Detection—0
The Effective Methods for Intrusion Detection With Limited Network Attack Data: Multi-Task Learning and Oversampling—0
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANsCode1
Machine Learning Applications in Misuse and Anomaly Detection—0
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