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Found 4 papers, 1 papers with code
CADeSH: Collaborative Anomaly Detection for Smart Homes
To overcome this, we propose a two-step collaborative anomaly detection method which first uses an autoencoder to differentiate frequent (`benign') and infrequent (possibly `malicious') traffic flows.
Privacy-Preserving Detection of IoT Devices Connected Behind a NAT in a Smart Home Setup
Today, telecommunication service providers (telcos) are exposed to cyber-attacks executed by compromised IoT devices connected to their customers' networks.
N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders
The proliferation of IoT devices which can be more easily compromised than desktop computers has led to an increase in the occurrence of IoT based botnet attacks.
Detection of Unauthorized IoT Devices Using Machine Learning Techniques
Based on the classification of 20 consecutive sessions and the use of majority rule, IoT device types that are not on the white list were correctly detected as unknown in 96% of test cases (on average), and white listed device types were correctly classified by their actual types in 99% of cases.
