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 361370 of 800 papers

TitleStatusHype
Extending Signature-based Intrusion Detection Systems WithBayesian Abductive Reasoning0
Extreme bandits0
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning0
Fast Feature Reduction in intrusion detection datasets0
ASNM Datasets: A Collection of Network Traffic Features for Testing of Adversarial Classifiers and Network Intrusion Detectors0
Feasibility of Non-Line-of-Sight Integrated Sensing and Communication at mmWave0
Feature Analysis for Machine Learning-based IoT Intrusion Detection0
Feature Distribution Shift Mitigation with Contrastive Pretraining for Intrusion Detection0
Efficient Network Traffic Feature Sets for IoT Intrusion Detection0
Efficient Network Representation for GNN-based Intrusion Detection0
Show:102550
← PrevPage 37 of 80Next →

Benchmark Results

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