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 621–630 of 800 papers

TitleStatusHype
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
Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape—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