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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 26–50 of 261 papers

TitleStatusHype
A flow-based IDS using Machine Learning in eBPFCode1
Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural NetworkCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANsCode1
Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection SystemsCode1
Efficient Deep CNN-BiLSTM Model for Network Intrusion DetectionCode1
Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion DetectorsCode1
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput ApplicationsCode1
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networksCode1
Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis—0
KnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs—0
Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning—0
A Robust PPO-optimized Tabular Transformer Framework for Intrusion Detection in Industrial IoT SystemsCode0
CSAGC-IDS: A Dual-Module Deep Learning Network Intrusion Detection Model for Complex and Imbalanced Data—0
Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability—0
Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking—0
A Virtual Cybersecurity Department for Securing Digital Twins in Water Distribution Systems—0
WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems—0
Are We There Yet? Unraveling the State-of-the-Art Graph Network Intrusion Detection Systems—0
Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems—0
PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning—0
Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detection—0
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems—0
Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection SystemsCode0
Mapping the Landscape of Generative AI in Network Monitoring and Management—0
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