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

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Showing 681–690 of 800 papers

TitleStatusHype
Convolutional Neural Network for Intrusion Detection System In Cyber Physical Systems—0
Mimic Learning to Generate a Shareable Network Intrusion Detection Model—0
Exploring Information Centrality for Intrusion Detection in Large Networks—0
End-to-End Adversarial Learning for Intrusion Detection in Computer Networks—0
Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms—0
A Compendium on Network and Host based Intrusion Detection Systems—0
Efficient GAN-based method for cyber-intrusion detection—0
Active Learning for Network Intrusion Detection—0
Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier—0
Extending Signature-based Intrusion Detection Systems WithBayesian Abductive Reasoning—0
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

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