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
Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification0
Towards a graph-based foundation model for network traffic analysis0
Towards an Efficient Anomaly-Based Intrusion Detection for Software-Defined Networks0
Towards a Privacy-preserving Deep Learning-based Network Intrusion Detection in Data Distribution Services0
Towards Explainable Network Intrusion Detection using Large Language Models0
Towards Low-Barrier Cybersecurity Research and Education for Industrial Control Systems0
Towards Network Traffic Monitoring Using Deep Transfer Learning0
Toward Supervised Anomaly Detection0
Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection0
Training a quantum annealing based restricted Boltzmann machine on cybersecurity data0
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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