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 3140 of 800 papers

TitleStatusHype
ARGUS: Context-Based Detection of Stealthy IoT Infiltration AttacksCode1
CAN bus intrusion detection based on auxiliary classifier GAN and out-of-distribution detectionCode1
Digital Twin-based Intrusion Detection for Industrial Control SystemsCode1
Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural NetworksCode1
An Intrusion Detection System based on Deep Belief NetworksCode1
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly DetectionCode1
Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection SystemsCode1
Open-Source Framework for Encrypted Internet and Malicious Traffic ClassificationCode1
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
Euler: Detecting Network Lateral Movement via Scalable Temporal Link PredictionCode1
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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