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 11–20 of 800 papers

TitleStatusHype
XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language ModelCode1
Real-time Event Recognition of Long-distance Distributed Vibration Sensing with Knowledge Distillation and Hardware AccelerationCode1
Federated PCA on Grassmann Manifold for IoT Anomaly DetectionCode1
PolyLUT-Add: FPGA-based LUT Inference with Wide InputsCode1
Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural NetworksCode1
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural NetworkCode1
On the Cross-Dataset Generalization of Machine Learning for Network Intrusion DetectionCode1
Improving Transferability of Network Intrusion Detection in a Federated Learning SetupCode1
A Study on Transferability of Deep Learning Models for Network Intrusion DetectionCode1
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly GenerationCode1
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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