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 301–310 of 800 papers

TitleStatusHype
Efficient Intrusion Detection Using Evidence Theory—0
Efficient IoT Intrusion Detection with an Improved Attention-Based CNN-BiLSTM Architecture—0
Efficient Network Representation for GNN-based Intrusion Detection—0
Efficient Network Traffic Feature Sets for IoT Intrusion Detection—0
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning—0
Enhanced network anomaly detection based on deep neural networks—0
EMO\&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.—0
DI-NIDS: Domain Invariant Network Intrusion Detection System—0
End-to-End Adversarial Learning for Intrusion Detection in Computer Networks—0
A review of Federated Learning in Intrusion Detection Systems for IoT—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