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
Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU) Learning in Cybersecurity—0
Machine Learning-based Android Intrusion Detection System—0
Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond—0
Optimized IoT Intrusion Detection using Machine Learning Technique—0
Graph-Powered Defense: Controller Area Network Intrusion Detection for Unmanned Aerial Vehicles—0
Swarm Intelligence-Driven Client Selection for Federated Learning in Cybersecurity applications—0
Optimal In-Network Distribution of Learning Functions for a Secure-by-Design Programmable Data Plane of Next-Generation Networks—0
An AutoML-based approach for Network Intrusion Detection—0
The importance of the clustering model to detect new types of intrusion in data traffic—0
Feature Selection 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