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
Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations—0
NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attacks—0
Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things—0
Trustworthy Anomaly Detection: A Survey—0
Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection—0
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks—0
Early Detection of Network Attacks Using Deep Learning—0
One-Shot Learning on Attributed Sequences—0
Security Orchestration, Automation, and Response Engine for Deployment of Behavioural Honeypots—0
An Interpretable Federated Learning-based Network Intrusion Detection Framework—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