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 101–110 of 800 papers

TitleStatusHype
An Identification System Using Eye Detection Based On Wavelets And Neural Networks—0
Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection—0
An Interpretable Federated Learning-based Network Intrusion Detection Framework—0
An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques—0
Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems—0
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies—0
A Critical Assessment of Interpretable and Explainable Machine Learning for Intrusion Detection—0
An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks—0
A new semi-supervised inductive transfer learning framework: Co-Transfer—0
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review—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