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 2130 of 800 papers

TitleStatusHype
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural NetworkCode1
ARGUS: Context-Based Detection of Stealthy IoT Infiltration AttacksCode1
CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for Network Intrusion DetectionCode1
CAN bus intrusion detection based on auxiliary classifier GAN and out-of-distribution detectionCode1
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
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
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9Unverified