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 621630 of 800 papers

TitleStatusHype
IMPACT: Impersonation Attack Detection via Edge Computing Using Deep Autoencoder and Feature Abstraction0
Hybrid Model For Intrusion Detection Systems0
Machine Learning based Anomaly Detection for 5G Networks0
1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data0
Securing of Unmanned Aerial Systems (UAS) against security threats using human immune system0
An Autonomous Intrusion Detection System Using an Ensemble of Advanced Learners0
IoT Behavioral Monitoring via Network Traffic Analysis0
Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process0
RePAD: Real-time Proactive Anomaly Detection for Time Series0
Pelican: A Deep Residual Network for Network Intrusion Detection0
Show:102550
← PrevPage 63 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