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 271–280 of 800 papers

TitleStatusHype
Efficient Network Representation for GNN-based Intrusion Detection—0
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification—0
PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based InferenceCode1
Multidomain transformer-based deep learning for early detection of network intrusion—0
Towards Low-Barrier Cybersecurity Research and Education for Industrial Control Systems—0
Assessing Cyclostationary Malware Detection via Feature Selection and Classification—0
Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection?Code0
Unsupervised anomalies detection in IIoT edge devices networks using federated learning—0
Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection—0
Real-time Regular Expression Matching—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