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 721730 of 800 papers

TitleStatusHype
Smart Water Security with AI and Blockchain-Enhanced Digital Twins0
SoK: Applying Machine Learning in Security - A Survey0
SoK: Knowledge is All You Need: Accelerating Last Mile Delivery for Automated Provenance-based Intrusion Detection with LLMs0
SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection0
SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks0
Gotham Dataset 2025: A Reproducible Large-Scale IoT Network Dataset for Intrusion Detection and Security ResearchCode0
Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternativesCode0
Sequence Covering for Efficient Host-Based Intrusion DetectionCode0
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion DetectionCode0
K-Metamodes: frequency- and ensemble-based distributed k-modes clustering for security analyticsCode0
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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