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Today, telecommunication service providers (telcos) are exposed tocyber-attacks executed by compromised IoT devices connected to their customers'networks. Such attacks might have severe effects not only on the target ofattacks but also on the telcos themselves. To mitigate those risks we propose amachine learning based method that can detect devices of specific vulnerableIoT models connected behind a domestic NAT, thereby identifying home networksthat pose a risk to the telco's infrastructure and availability of services. Aspart of the effort to preserve the domestic customers' privacy, our methodrelies on NetFlow data solely, refraining from inspecting the payload. Topromote future research in this domain we share our novel dataset, collected inour lab from numerous and various commercial IoT devices.

Privacy-Preserving Detection of IoT Devices Connected Behind a NAT in a Smart Home Setup