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 7180 of 800 papers

TitleStatusHype
Sketch-Based Anomaly Detection in Streaming GraphsCode1
ARGUS: Context-Based Detection of Stealthy IoT Infiltration AttacksCode1
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
A Study on Transferability of Deep Learning Models for Network Intrusion DetectionCode1
CAN bus intrusion detection based on auxiliary classifier GAN and out-of-distribution detectionCode1
Gotham Dataset 2025: A Reproducible Large-Scale IoT Network Dataset for Intrusion Detection and Security ResearchCode0
Fragments Expert A Graphical User Interface MATLAB Toolbox for Classification of File FragmentsCode0
Hybrid Isolation Forest - Application to Intrusion DetectionCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network SecurityCode0
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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