SOTAVerified

Intrusion Detection

Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.

Source: Machine Learning Techniques for Intrusion Detection

Papers

Showing 110 of 800 papers

TitleStatusHype
LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of VehiclesCode2
A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of VehiclesCode2
Simplified and Secure MCP Gateways for Enterprise AI IntegrationCode1
CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for Network Intrusion DetectionCode1
Enabling AutoML for Zero-Touch Network Security: Use-Case Driven AnalysisCode1
Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless NetworksCode1
Continual Learning with Strategic Selection and Forgetting for Network Intrusion DetectionCode1
FedMSE: Federated learning for IoT network intrusion detectionCode1
Exploring QUIC Dynamics: A Large-Scale Dataset for Encrypted Traffic AnalysisCode1
Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion DetectionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13Unverified
2K-Nearest NeighborsAccuracy (%)98.07Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9Unverified