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 511520 of 800 papers

TitleStatusHype
Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion DetectionCode1
Machine learning on knowledge graphs for context-aware security monitoringCode1
Cybersecurity Anomaly Detection in Adversarial Environments0
ADASYN-Random Forest Based Intrusion Detection Model0
Extending Isolation Forest for Anomaly Detection in Big Data via K-Means0
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT0
Robustness of ML-Enhanced IDS to Stealthy Adversaries0
Adversarial Training for Deep Learning-based Intrusion Detection Systems0
Benchmarking the Benchmark -- Analysis of Synthetic NIDS Datasets0
Exploring Cybersecurity Issues in 5G Enabled Electric Vehicle Charging Station with Deep Learning0
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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