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 351360 of 800 papers

TitleStatusHype
Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection0
Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces0
Exploring Cybersecurity Issues in 5G Enabled Electric Vehicle Charging Station with Deep Learning0
Exploring Edge TPU for Network Intrusion Detection in IoT0
Exploring Global and Local Information for Anomaly Detection with Normal Samples0
Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN0
Exploring Information Centrality for Intrusion Detection in Large Networks0
ASNM Datasets: A Collection of Network Traffic Features for Testing of Adversarial Classifiers and Network Intrusion Detectors0
Efficient Network Traffic Feature Sets for IoT Intrusion Detection0
Efficient Network Representation for GNN-based Intrusion Detection0
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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