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Network Intrusion Detection
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Network intrusion detection is the task of monitoring network traffic to and from all devices on a network in order to detect computer attacks.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
KnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs
Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning
A Robust PPO-optimized Tabular Transformer Framework for Intrusion Detection in Industrial IoT Systems
CSAGC-IDS: A Dual-Module Deep Learning Network Intrusion Detection Model for Complex and Imbalanced Data
Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability
Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking
A Virtual Cybersecurity Department for Securing Digital Twins in Water Distribution Systems
WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems
Are We There Yet? Unraveling the State-of-the-Art Graph Network Intrusion Detection Systems
Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems
PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning
Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detection
CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for Network Intrusion Detection
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems
Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection Systems
Mapping the Landscape of Generative AI in Network Monitoring and Management
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection
Detecting APT Malware Command and Control over HTTP(S) Using Contextual Summaries
Technical Report: Generating the WEB-IDS23 Dataset
Investigating Application of Deep Neural Networks in Intrusion Detection System Design
A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network Security
PolyLUT: Ultra-low Latency Polynomial Inference with Hardware-Aware Structured Pruning
Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks
A Temporal Convolutional Network-based Approach for Network Intrusion Detection
Continual Learning with Strategic Selection and Forgetting for Network Intrusion Detection
Flow Exporter Impact on Intelligent Intrusion Detection Systems
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection
Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond
Graph-Powered Defense: Controller Area Network Intrusion Detection for Unmanned Aerial Vehicles
An AutoML-based approach for Network Intrusion Detection
Feature Selection for Network Intrusion Detection
Take Package as Language: Anomaly Detection Using Transformer
Intelligent Green Efficiency for Intrusion Detection
SCGNet-Stacked Convolution with Gated Recurrent Unit Network for Cyber Network Intrusion Detection and Intrusion Type Classification
Implementing Lightweight Intrusion Detection System on Resource Constrained Devices
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