  With an increasing number of Internet of Things (IoT) devices present in
homes, there is a rise in the number of potential information leakage channels
and their associated security threats and privacy risks. Despite a long history
of attacks on IoT devices in unprotected home networks, the problem of
accurate, rapid detection and prevention of such attacks remains open. Many
existing IoT protection solutions are cloud-based, sometimes ineffective, and
might share consumer data with unknown third parties. This paper investigates
the potential for effective IoT threat detection locally, on a home router,
using AI tools combined with classic rule-based traffic-filtering algorithms.
Our results show that with a slight rise of router hardware resources caused by
machine learning and traffic filtering logic, a typical home router
instrumented with our solution is able to effectively detect risks and protect
a typical home IoT network, equaling or outperforming existing popular
solutions, without any effects on benign IoT functionality, and without relying
on cloud services and third parties.
