Researchers have suggested how to track a smartphone with GPS off
A group of researchers from the Institute of Electrical and Electronics Engineers (IEEE) has developed a PinMe system that allows you to track its location based on open data and information from various sensors of your smartphone with an accuracy comparable to GPS. In this regard, the researchers urge gadget manufacturers to add a software solution that disables the operation of all sensors, and not just GPS.
Scientists have discovered a security gap in smartphones. Comparing the information from the accelerometer and the gyroscope with the open data - maps and weather reports, they were able to determine the location of the person, his route and type of transport.
The PinMe application developed by researchers shows how using machine learning methods and information from innocuous sensors, you can get important information about a person’s life, said Prataek Mittal, associate professor at the Department of Electrical Engineering, Princeton University, co-author of the study. Unlike satellite navigation, information from sensors does not require permission to access.
“Hackers can convince a ship or unmanned vehicle that they are in a place that is different from their actual location, which can be a problem for American ships navigating international waters, for example, or for the safety of passengers in autonomous cars,” says Nirei Jha , a professor at Princeton University and co-author of the study. According to him, the PinMe team is already communicating with technology companies about licensing the application as a navigation tool.
For the experiment, scientists collected data from the Galaxy S4 i9500, iPhone 6 and iPhone 6S smartphones, which were used by three people during the day after installing PinMe. Experimental walked, traveled by car, train, flew in an airplane around Philadelphia, Dallas, Princeton and other cities.
To begin with, PinMe read the information about the latest IP address of the smartphone and the status of the network to determine the last connection to Wi-Fi - this way the application received a starting point for further work. The application then used an algorithm that was “trained” by machine learning to recognize the difference between walking, driving, flying, and other means of movement. For this, data from the sensors were used - the direction and speed of movement, the frequency of stops, as well as the height above sea level.
After determining how to move PinMe included a new algorithm and began to make the user's route. The OpenStreetMaps service was used to get the actual navigation data. Google Maps helped to determine the location due to comparison with the map of altitudes above sea level. To clarify the route, the application used the Weather Channel weather service : accurate information about the temperature and air pressure helps to neutralize the influence of weather conditions on the information collected by the sensors. Route data were compared with the schedule of airlines or railway lines.
When traveling from Philadelphia to Dallas by plane, the application first determines the type of transport by altitude changes and acceleration, and then - by time zone, weather, and schedule it finds the place of take-off and landing.
In the illustration below, the route tracked with PinMe — driving and walking on foot — in black — plotted according to GPS data is marked in green and yellow.
These researchers were not the first to use accelerometers to track people. In 2010, the Japanese telecommunications corporation KDDI, the mobile operator au, developed an application to track employee movements in the company. The goal of the developers was total control over the employees in order to increase the efficiency of their work. The data from the accelerometer made it possible to determine movement on a flat surface and on a ladder, speed of movement, trips to the toilet. Moreover, a smartphone at the waist of the cleaning lady could tell the difference between mopping, sweeping and shaking out the garbage can.
In 2015, specialists from the Nanjing University in China used data from the accelerometer to monitor the movement of people on the subway: “Subway trains move along rails, so their traffic patterns are different from cars and buses traveling along roads, and since there are no two absolutely identical sections of neighboring metro stations, patterns of movement of trains at different time intervals can also be distinguished among themselves. " For work, you need to make a map of the subway, then to determine routes with accuracy from 70% to 92%.
The scientific work was published on February 5, 2018 on the website of the Cornell University Library. DOI: 10.1109 / TMSCS.2017.2751462.
Scientists have discovered a security gap in smartphones. Comparing the information from the accelerometer and the gyroscope with the open data - maps and weather reports, they were able to determine the location of the person, his route and type of transport.
The PinMe application developed by researchers shows how using machine learning methods and information from innocuous sensors, you can get important information about a person’s life, said Prataek Mittal, associate professor at the Department of Electrical Engineering, Princeton University, co-author of the study. Unlike satellite navigation, information from sensors does not require permission to access.
“Hackers can convince a ship or unmanned vehicle that they are in a place that is different from their actual location, which can be a problem for American ships navigating international waters, for example, or for the safety of passengers in autonomous cars,” says Nirei Jha , a professor at Princeton University and co-author of the study. According to him, the PinMe team is already communicating with technology companies about licensing the application as a navigation tool.
For the experiment, scientists collected data from the Galaxy S4 i9500, iPhone 6 and iPhone 6S smartphones, which were used by three people during the day after installing PinMe. Experimental walked, traveled by car, train, flew in an airplane around Philadelphia, Dallas, Princeton and other cities.
To begin with, PinMe read the information about the latest IP address of the smartphone and the status of the network to determine the last connection to Wi-Fi - this way the application received a starting point for further work. The application then used an algorithm that was “trained” by machine learning to recognize the difference between walking, driving, flying, and other means of movement. For this, data from the sensors were used - the direction and speed of movement, the frequency of stops, as well as the height above sea level.
After determining how to move PinMe included a new algorithm and began to make the user's route. The OpenStreetMaps service was used to get the actual navigation data. Google Maps helped to determine the location due to comparison with the map of altitudes above sea level. To clarify the route, the application used the Weather Channel weather service : accurate information about the temperature and air pressure helps to neutralize the influence of weather conditions on the information collected by the sensors. Route data were compared with the schedule of airlines or railway lines.
When traveling from Philadelphia to Dallas by plane, the application first determines the type of transport by altitude changes and acceleration, and then - by time zone, weather, and schedule it finds the place of take-off and landing.
In the illustration below, the route tracked with PinMe — driving and walking on foot — in black — plotted according to GPS data is marked in green and yellow.
These researchers were not the first to use accelerometers to track people. In 2010, the Japanese telecommunications corporation KDDI, the mobile operator au, developed an application to track employee movements in the company. The goal of the developers was total control over the employees in order to increase the efficiency of their work. The data from the accelerometer made it possible to determine movement on a flat surface and on a ladder, speed of movement, trips to the toilet. Moreover, a smartphone at the waist of the cleaning lady could tell the difference between mopping, sweeping and shaking out the garbage can.
In 2015, specialists from the Nanjing University in China used data from the accelerometer to monitor the movement of people on the subway: “Subway trains move along rails, so their traffic patterns are different from cars and buses traveling along roads, and since there are no two absolutely identical sections of neighboring metro stations, patterns of movement of trains at different time intervals can also be distinguished among themselves. " For work, you need to make a map of the subway, then to determine routes with accuracy from 70% to 92%.
The scientific work was published on February 5, 2018 on the website of the Cornell University Library. DOI: 10.1109 / TMSCS.2017.2751462.
Source: https://habr.com/ru/post/410117/