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 5160 of 800 papers

TitleStatusHype
Sketch-Based Anomaly Detection in Streaming GraphsCode1
MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of VehiclesCode1
Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion DetectionCode1
Machine learning on knowledge graphs for context-aware security monitoringCode1
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
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
Intrusion Detection for Cyber-Physical Systems using Generative Adversarial Networks in Fog EnvironmentCode1
Show:102550
← PrevPage 6 of 80Next →

Benchmark Results

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