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 341–350 of 800 papers

TitleStatusHype
AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs—0
Separating Flows in Encrypted Tunnel TrafficCode0
A Dependable Hybrid Machine Learning Model for Network Intrusion Detection—0
Application of a Dynamic Line Graph Neural Network for Intrusion Detection With Semisupervised Learning—0
A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection—0
Network Security Modelling with Distributional Data—0
Intrusion Detection in Internet of Things using Convolutional Neural Networks—0
A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System—0
Reliable Malware Analysis and Detection using Topology Data AnalysisCode0
GowFed -- A novel Federated Network Intrusion Detection System—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