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 191–200 of 800 papers

TitleStatusHype
Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems—0
A Critical Assessment of Interpretable and Explainable Machine Learning for Intrusion Detection—0
CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Model—0
An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks—0
CADeSH: Collaborative Anomaly Detection for Smart Homes—0
ByteStack-ID: Integrated Stacked Model Leveraging Payload Byte Frequency for Grayscale Image-based Network Intrusion Detection—0
A new semi-supervised inductive transfer learning framework: Co-Transfer—0
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review—0
Building an Effective Intrusion Detection System using Unsupervised Feature Selection in Multi-objective Optimization Framework—0
BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion 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