 "Beware! Hackers can guess PINs using your smartphone's sensor data")
Data from your smartphone sensors can reveal PINs and passwords to hackers and allow them to unlock your mobile devices, according to a study led by an Indian-origin scientist.
Instruments in smartphones such as the gyroscope and proximity sensors represent a potential security vulnerability, said researchers from Nanyang Technological University (NTU) in Singapore.
Using machine learning algorithms and a combination of information gathered from six different sensors found in smart phones, researchers succeeded in unlocking Android smartphones with a 99.5 per cent accuracy within only three tries, when tackling a phone that had one of the 50 most common PIN numbers.
The previous best phone-cracking success rate was 74 per cent for the 50 most common pin numbers, but NTU's technique can be used to guess all 10,000 possible combinations of four-digit PINs.
Led by Shivam Bhasin, NTU Senior Research Scientist, researchers used sensors in a smart phone to model which number had been pressed by its users, based on how the phone was tilted and how much light is blocked by the thumb or fingers.
The researchers believe their work highlights a significant flaw in smart phone security, as using the sensors within the phones require no permissions to be given by the phone user and are openly available for all apps to access.
The team of researchers took Android phones and installed a custom application which collected data from six sensors: accelerometer, gyroscope, magnetometer, proximity sensor, barometer, and ambient light sensor.
"When you hold your phone and key in the PIN, the way the phone moves when you press 1, 5, or 9, is very different. Likewise, pressing 1 with your right thumb will block more light than if you pressed 9," said Bhasin.
The classification algorithm was trained with data collected from three people, who each entered a random set of 70 four-digit pin numbers on a phone.
At the same time, it recorded the relevant sensor reactions.
Known as deep learning, the classification algorithm was able to give different weightings of importance to each of the sensors, depending on how sensitive each was to different numbers being pressed.
This helps eliminate factors which it judges to be less important and increases the success rate for PIN retrieval.
Although each individual enters the security PIN on their phone differently, the scientists showed that as data from more people is fed to the algorithm over time, success rates improved.
So while a malicious application may not be able to correctly guess a PIN immediately after installation, using machine learning, it could collect data from thousands of users over time from each of their phones to learn their PIN entry pattern and then launch an attack later when the success rate is much higher.
The study shows how devices with seemingly strong security can be attacked using a side-channel, as sensor data could be diverted by malicious applications to spy on user behaviour and help to access PIN and password information, said Professor Gan Chee Lip from NTU.
To keep mobile devices secure, Bhasin advises users to have PINs with more than four digits, coupled with other authentication methods like one-time passwords, two-factor authentications, and fingerprint or facial recognition.
Instruments in smartphones such as the gyroscope and proximity sensors represent a potential security vulnerability, said researchers from Nanyang Technological University (NTU) in Singapore.
Using machine learning algorithms and a combination of information gathered from six different sensors found in smart phones, researchers succeeded in unlocking Android smartphones with a 99.5 per cent accuracy within only three tries, when tackling a phone that had one of the 50 most common PIN numbers.
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Led by Shivam Bhasin, NTU Senior Research Scientist, researchers used sensors in a smart phone to model which number had been pressed by its users, based on how the phone was tilted and how much light is blocked by the thumb or fingers.
The researchers believe their work highlights a significant flaw in smart phone security, as using the sensors within the phones require no permissions to be given by the phone user and are openly available for all apps to access.
The team of researchers took Android phones and installed a custom application which collected data from six sensors: accelerometer, gyroscope, magnetometer, proximity sensor, barometer, and ambient light sensor.
"When you hold your phone and key in the PIN, the way the phone moves when you press 1, 5, or 9, is very different. Likewise, pressing 1 with your right thumb will block more light than if you pressed 9," said Bhasin.
The classification algorithm was trained with data collected from three people, who each entered a random set of 70 four-digit pin numbers on a phone.
At the same time, it recorded the relevant sensor reactions.
Known as deep learning, the classification algorithm was able to give different weightings of importance to each of the sensors, depending on how sensitive each was to different numbers being pressed.
This helps eliminate factors which it judges to be less important and increases the success rate for PIN retrieval.
Although each individual enters the security PIN on their phone differently, the scientists showed that as data from more people is fed to the algorithm over time, success rates improved.
So while a malicious application may not be able to correctly guess a PIN immediately after installation, using machine learning, it could collect data from thousands of users over time from each of their phones to learn their PIN entry pattern and then launch an attack later when the success rate is much higher.
The study shows how devices with seemingly strong security can be attacked using a side-channel, as sensor data could be diverted by malicious applications to spy on user behaviour and help to access PIN and password information, said Professor Gan Chee Lip from NTU.
To keep mobile devices secure, Bhasin advises users to have PINs with more than four digits, coupled with other authentication methods like one-time passwords, two-factor authentications, and fingerprint or facial recognition.