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

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Showing 461–470 of 800 papers

TitleStatusHype
Training Automated Defense Strategies Using Graph-based Cyber Attack Simulations—0
Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey—0
Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI—0
Triadic-OCD: Asynchronous Online Change Detection with Provable Robustness, Optimality, and Convergence—0
Trustworthy Anomaly Detection: A Survey—0
Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space—0
Two-stage Deep Stacked Autoencoder with Shallow Learning for Network Intrusion Detection System—0
Two Timescale Convergent Q-learning for Sleep--Scheduling in Wireless Sensor Networks—0
Unmasking Stealthy Attacks on Nonlinear DAE Models of Power Grids—0
Unsupervised anomalies detection in IIoT edge devices networks using federated learning—0
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13—Unverified
2K-Nearest NeighborsAccuracy (%)98.07—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9—Unverified