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 251260 of 800 papers

TitleStatusHype
RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture0
Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning0
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly GenerationCode1
SecureBERT and LLAMA 2 Empowered Control Area Network Intrusion Detection and Classification0
Open Set Dandelion Network for IoT Intrusion Detection0
LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion DetectionCode1
STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph LearningCode1
Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection0
netFound: Foundation Model for Network SecurityCode1
A model for multi-attack classification to improve intrusion detection performance using deep learning approaches0
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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