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 101110 of 800 papers

TitleStatusHype
Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection SystemsCode0
Simultaneous Intrusion Detection and Localization Using ISAC Network0
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced DatasetsCode0
Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems0
Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability0
Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking0
Smart Water Security with AI and Blockchain-Enhanced Digital Twins0
A Virtual Cybersecurity Department for Securing Digital Twins in Water Distribution Systems0
Zero-Day Botnet Attack Detection in IoV: A Modular Approach Using Isolation Forests and Particle Swarm Optimization0
Breaking the Flow and the Bank: Stealthy Cyberattacks on Water Network Hydraulics0
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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