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

Showing 451460 of 800 papers

TitleStatusHype
A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System0
T-DFNN: An Incremental Learning Algorithm for Intrusion Detection SystemsCode1
A Dynamic Watermarking Algorithm for Finite Markov Decision Problems0
threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph LearningCode1
A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection0
Intrusion Detection: Machine Learning Baseline Calculations for Image Classification0
A Comparative Analysis of Machine Learning Algorithms for Intrusion Detection in Edge-Enabled IoT Networks0
Intrusion Detection using Spatial-Temporal features based on Riemannian Manifold0
TOD: GPU-accelerated Outlier Detection via Tensor OperationsCode1
Bridging the gap to real-world for network intrusion detection systems with data-centric approachCode1
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Benchmark Results

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