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 4150 of 800 papers

TitleStatusHype
Continual Learning with Strategic Selection and Forgetting for Network Intrusion DetectionCode1
Cyber Attack Detection thanks to Machine Learning AlgorithmsCode1
Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural NetworkCode1
STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph LearningCode1
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
Enabling AutoML for Zero-Touch Network Security: Use-Case Driven AnalysisCode1
Exploring QUIC Dynamics: A Large-Scale Dataset for Encrypted Traffic AnalysisCode1
FedMSE: Federated learning for IoT network intrusion detectionCode1
Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT AlgorithmsCode1
Open-Source Framework for Encrypted Internet and Malicious Traffic ClassificationCode1
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Benchmark Results

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