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Found 2 papers, 1 papers with code
Detecting Anomalous Microflows in IoT Volumetric Attacks via Dynamic Monitoring of MUD Activity
(4) We demonstrate how our models scale in environments with a large number of connected IoTs (with datasets collected from a network of IP cameras in our university campus) by considering various training strategies (per device unit versus per device type), and balancing the accuracy of prediction against the cost of models in terms of size and training time.
Verifying and Monitoring IoTs Network Behavior using MUD Profiles
Our first contribution is to develop a tool that takes the traffic trace of an arbitrary IoT device as input and automatically generates the MUD profile for it.
Networking and Internet Architecture
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