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 141150 of 800 papers

TitleStatusHype
Data Distribution ValuationCode0
Behavioural Reports of Multi-Stage MalwareCode0
Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternativesCode0
LuNet: A Deep Neural Network for Network Intrusion DetectionCode0
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection SystemsCode0
A Robust PPO-optimized Tabular Transformer Framework for Intrusion Detection in Industrial IoT SystemsCode0
Benchmarking Unsupervised Online IDS for Masquerade Attacks in CANCode0
CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks SimulationCode0
Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine LearningCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
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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