Sound waves ‘could be used to hack IoT’

Sound waves ‘could be used to hack IoT’ [Image: Pobytov via iStock]

Sound waves could be used to hack into critical sensors in a number of different forms of technology, including smartphones, vehicles, medical devices and the Internet of Things (IoT), new research has shown.

Scientists from the University of Michigan said that their findings question the long-held belief that software can automatically trust hardware sensors, which provide autonomous systems with the data required for them to make decisions.

They explained that they used inertial sensors in their research, which are known as capacitive MEMS accelerometers. They measure the rate of change in an object's speed in three dimensions and they can be tricked.

Kevin Fu, University of Michigan associate professor of computer science and engineering, led his team in using precisely tuned acoustic tones to deceive 15 different models of accelerometers into registering movement that never actually occurred.

This provided a backdoor into the devices, which then enabled the researchers to control other aspects of the system.

Mr Fu said: “The fundamental physics of the hardware allowed us to trick sensors into delivering a false reality to the microprocessor. Our findings upend widely held assumptions about the security of the underlying hardware.

"If you look through the lens of computer science, you won't see this security problem. If you look through the lens of materials science, you won't see this security problem. Only when looking through both lenses at the same time can one see these vulnerabilities."

All accelerometers have an analogue core – a mass suspended on springs. It moves accordingly when the object the accelerometer is embedded in changes speed or direction. The digital components in the accelerometer process the signal and send it to other circuits in the device.

According to Mr Fu, thousands of devices used every day already contain tiny MEMS accelerometers. He added that future devices will “aggressively rely on sensors to make automated decisions with kinetic consequences”.