2015/05/22 by Jingyu Hua, Hua, Jingyu, Zhenyu Shen +3 · 1 voice
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Evacuation and Crowd Dynamics #Indoor and Outdoor Localization Technologies #cs.CR
paper · pdf · doi:10.48550/arxiv.1505.05958
openalex publication_date 2015/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Motion sensors (e.g., accelerometers) on smartphones have been demonstrated to be a powerful side channel for attackers to spy on users' inputs on touchscreen. In this paper, we reveal another motion accelerometer-based attack which is particularly serious: when a person takes the metro, a malicious application on her smartphone can easily use accelerator readings to trace her. We first propose a basic attack that can automatically extract metro-related data from a large amount of mixed accelerator readings, and then use an ensemble interval classier built from supervised learning to infer the riding intervals of the user. While this attack is very effective, the supervised learning part requires the attacker to collect labeled training data for each station interval, which is a significant amount of effort. To improve the efficiency of our attack, we further propose a semi-supervised learning approach, which only requires the attacker to collect labeled data for a very small number of station intervals with obvious characteristics. We conduct real experiments on a metro line in a major city. The results show that the inferring accuracy could reach 89% and 92% if the user takes the metro for 4 and 6 stations, respectively.