Clustered Federated Learning Architecture for Network Anomaly Detection in Large Scale Heterogeneous IoT Networks. (arXiv:2303.15986v1 [cs.CR])

There is a growing trend of cyberattacks against Internet of Things (IoT)
devices; moreover, the sophistication and motivation of those attacks is
increasing. The vast scale of IoT, diverse hardware and software, and being
typically placed in uncontrolled environments make traditional IT security
mechanisms such as signature-based intrusion detection and prevention systems
challenging to integrate. They also struggle to cope with the rapidly evolving
IoT threat landscape due to long delays between the analysis and publication of
the detection rules. …

analysis anomaly detection architecture attacks cyberattacks detection detection rules devices environments federated learning hardware integrate internet internet of things intrusion intrusion detection iot it security large machine machine learning motivation network networks prevention rules scale security signature software systems things threat threat landscape trend vast