Privacy-Preserving Detection of IoT Devices Connected Behind a NAT in a Smart Home Setup
This addresses security risks for telecommunication providers from compromised IoT devices in smart homes, with an incremental approach using existing data types.
The paper tackles the problem of detecting vulnerable IoT devices behind home NATs to mitigate cyber-attacks on telecommunication providers, proposing a machine learning method based on NetFlow data that identifies at-risk networks while preserving privacy.
Today, telecommunication service providers (telcos) are exposed to cyber-attacks executed by compromised IoT devices connected to their customers' networks. Such attacks might have severe effects not only on the target of attacks but also on the telcos themselves. To mitigate those risks we propose a machine learning based method that can detect devices of specific vulnerable IoT models connected behind a domestic NAT, thereby identifying home networks that pose a risk to the telco's infrastructure and availability of services. As part of the effort to preserve the domestic customers' privacy, our method relies on NetFlow data solely, refraining from inspecting the payload. To promote future research in this domain we share our novel dataset, collected in our lab from numerous and various commercial IoT devices.