Abstract
We developed a novel, proof-of-concept side-channel attack framework called RouteDetector, which identifies a route for a train trip by simply reading smart device sensors: an accelerometer, magnetometer, and gyroscope. All these sensors are commonly used by many apps without requiring any permissions. The key technical components of RouteDetector can be summarized as follows. First, by applying a machine-learning technique to the data collected from sensors, RouteDetector detects the activity of a user, i.e., "walking," "in moving vehicle," or "other." Next, it extracts departure/arrival times of vehicles from the sequence of the detected human activities. Finally, by correlating the detected departure/arrival times of the vehicle with timetables/route maps collected from all the railway companies in the rider's country, it identifies potential routes that can be used for a trip. We demonstrate that the strategy is feasible through field experiments and extensive simulation experiments using timetables and route maps for 9,090 railway stations of 172 railway companies.
| Original language | English |
|---|---|
| Pages (from-to) | 1680-1690 |
| Number of pages | 11 |
| Journal | IEICE Transactions on Information and Systems |
| Volume | E100D |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2017 Aug 1 |
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Keywords
- Location identification
- Mobile security
- Side-channel attack
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Artificial Intelligence
- Electrical and Electronic Engineering
Cite this
Tracking the human mobility using mobile device sensors. / Watanabe, Takuya; Akiyama, Mitsuaki; Mori, Tatsuya.
In: IEICE Transactions on Information and Systems, Vol. E100D, No. 8, 01.08.2017, p. 1680-1690.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Tracking the human mobility using mobile device sensors
AU - Watanabe, Takuya
AU - Akiyama, Mitsuaki
AU - Mori, Tatsuya
PY - 2017/8/1
Y1 - 2017/8/1
N2 - We developed a novel, proof-of-concept side-channel attack framework called RouteDetector, which identifies a route for a train trip by simply reading smart device sensors: an accelerometer, magnetometer, and gyroscope. All these sensors are commonly used by many apps without requiring any permissions. The key technical components of RouteDetector can be summarized as follows. First, by applying a machine-learning technique to the data collected from sensors, RouteDetector detects the activity of a user, i.e., "walking," "in moving vehicle," or "other." Next, it extracts departure/arrival times of vehicles from the sequence of the detected human activities. Finally, by correlating the detected departure/arrival times of the vehicle with timetables/route maps collected from all the railway companies in the rider's country, it identifies potential routes that can be used for a trip. We demonstrate that the strategy is feasible through field experiments and extensive simulation experiments using timetables and route maps for 9,090 railway stations of 172 railway companies.
AB - We developed a novel, proof-of-concept side-channel attack framework called RouteDetector, which identifies a route for a train trip by simply reading smart device sensors: an accelerometer, magnetometer, and gyroscope. All these sensors are commonly used by many apps without requiring any permissions. The key technical components of RouteDetector can be summarized as follows. First, by applying a machine-learning technique to the data collected from sensors, RouteDetector detects the activity of a user, i.e., "walking," "in moving vehicle," or "other." Next, it extracts departure/arrival times of vehicles from the sequence of the detected human activities. Finally, by correlating the detected departure/arrival times of the vehicle with timetables/route maps collected from all the railway companies in the rider's country, it identifies potential routes that can be used for a trip. We demonstrate that the strategy is feasible through field experiments and extensive simulation experiments using timetables and route maps for 9,090 railway stations of 172 railway companies.
KW - Location identification
KW - Mobile security
KW - Side-channel attack
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U2 - 10.1587/transinf.2016ICP0022
DO - 10.1587/transinf.2016ICP0022
M3 - Article
AN - SCOPUS:85026500387
VL - E100D
SP - 1680
EP - 1690
JO - IEICE Transactions on Information and Systems
JF - IEICE Transactions on Information and Systems
SN - 0916-8532
IS - 8
ER -