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 571580 of 800 papers

TitleStatusHype
Smart Water Security with AI and Blockchain-Enhanced Digital Twins0
SoK: Applying Machine Learning in Security - A Survey0
SoK: Knowledge is All You Need: Accelerating Last Mile Delivery for Automated Provenance-based Intrusion Detection with LLMs0
SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection0
SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks0
Sorting out typicality with the inverse moment matrix SOS polynomial0
Spiking Neural Networks with Single-Spike Temporal-Coded Neurons for Network Intrusion Detection0
StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems0
Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems0
STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles0
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Benchmark Results

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