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 591–600 of 800 papers

TitleStatusHype
The Effective Methods for Intrusion Detection With Limited Network Attack Data: Multi-Task Learning and Oversampling—0
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed NetworksCode0
Graph-Based Intrusion Detection System for Controller Area Networks—0
Experimental Review of Neural-based approaches for Network Intrusion Management—0
Machine Learning Applications in Misuse and Anomaly Detection—0
PIDNet: An Efficient Network for Dynamic Pedestrian Intrusion Detection—0
Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion DetectionCode0
Network Intrusion Detection Using Wrapper-based Decision Tree for Feature Selection—0
Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection—0
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection—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